diff --git a/.gitignore b/.gitignore
index ec5563a..a3fce6f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,6 +14,11 @@ logs/
wandb/
*.pt
*.onnx
+*.pkl
+*.pickle
+*.npz
+src/mjlab_playground/asset_zoo/motions/retargeted/*
+!src/mjlab_playground/asset_zoo/motions/retargeted/README.md
scripts/
diff --git a/NOTICE b/NOTICE
new file mode 100644
index 0000000..caa9175
--- /dev/null
+++ b/NOTICE
@@ -0,0 +1,19 @@
+mjlab_playground Booster locomotion extensions
+Copyright 2026 66Leslie and contributors
+
+This product includes software developed by the mjlab_playground contributors:
+https://github.com/mujocolab/mjlab_playground
+
+The Booster T1 velocity task contains adapted and modified work from
+KaydenKnapik/BoosterT1mjlab:
+https://github.com/KaydenKnapik/BoosterT1mjlab
+
+Booster K1 asset and configuration work references NishB17/MJLabModified:
+https://github.com/NishB17/MJLabModified
+
+Parts of the competition-compatible Booster K1 MJCF lineage reference
+RCSSServerMJ. Its MIT license is reproduced in
+third_party_licenses/RCSSServerMJ-MIT.txt.
+
+Motion datasets and locally generated retargeted files are not included in, and
+are not relicensed by, this distribution.
diff --git a/README.md b/README.md
index 3e23676..bbe5b2c 100644
--- a/README.md
+++ b/README.md
@@ -1,45 +1,113 @@
-# mjlab playground
+# mjlab playground: Booster locomotion tasks
+
+This fork extends [mujocolab/mjlab_playground](https://github.com/mujocolab/mjlab_playground) with Booster T1/K1 locomotion tasks while keeping the upstream task-registration and configuration style.
+
+## Demo
+
+### Booster T1 velocity tracking
+
+
+
+The dark-blue arrow is the commanded linear velocity and the cyan arrow is the
+measured linear velocity. Dark/light green show commanded/measured yaw motion.
+This five-second clip was recorded with MJLab's built-in `play --video` support
+using the compatible `2026-04-15_10-04-41/model_10100.pt` checkpoint. The later
+`2026-04-27_10-05-04/model_13900.pt` run has a known low-response command region
+and is intentionally not used for this demo.
+
+## Repository layout
+
+```text
+src/mjlab_playground/
+├── amp/ # Shared AMP algorithm, runner, observations, loaders
+├── asset_zoo/robots/ # Robot-specific MJCF and constants
+│ ├── booster_t1/
+│ └── booster_k1/
+└── tasks/ # Environment/task implementations
+ ├── common/ # Small helpers shared by task families
+ ├── getup/
+ │ └── config/{go1,k1,t1}/
+ ├── velocity/
+ │ └── config/t1/
+ ├── tracking/
+ │ └── config/t1/
+ └── target_location_amp/
+ └── config/{k1,t1}/
+```
-A collection of tasks built with [mjlab](https://github.com/mujocolab/mjlab), starting with ports from [MuJoCo Playground](https://playground.mujoco.org/).
+Task-independent AMP code lives in `amp/`; target-location commands, rewards, termination rules and robot-specific configuration stay in `tasks/target_location_amp/`. This avoids coupling the AMP implementation to a single task and leaves a clear place for future robots.
## Tasks
-| Task ID | Robot | Description | Preview |
-|---------|-------|-------------|---------|
-| **Getup** | | | |
-| `Mjlab-Getup-Flat-Unitree-Go1` | Unitree Go1 | Fall recovery on flat terrain | --num_envs 4096
+uv run train Mjlab-Velocity-Flat-Booster-T1 --env.scene.num-envs 4096
+uv run play Mjlab-Velocity-Flat-Booster-T1
```
-Play back a trained policy:
+Record a short clip from a local checkpoint:
```bash
-uv run play
+uv run play Mjlab-Velocity-Flat-Booster-T1 \
+ --checkpoint-file /path/to/model_10100.pt \
+ --num-envs 1 --video True --video-length 250
```
-### Getup training
+The tracking and AMP tasks require separately prepared motion data. See [Motion data](docs/motion_data.md) before running them.
+
+## Motion-data policy
+
+Retargeted files are generated artifacts, but their redistribution rights still depend on the original motion dataset. Consequently, this repository does **not** include LAFAN1-, AMASS-, or BMLrub-derived `.npz`, `.pkl`, or `.pickle` files. Retarget them locally from data you are licensed to use, then point the task to the local output. The expected paths, environment variables and pickle schema are documented in [docs/motion_data.md](docs/motion_data.md).
+
+## Sources and attribution
+
+- Base repository: [mujocolab/mjlab_playground](https://github.com/mujocolab/mjlab_playground), Apache-2.0.
+- The T1 velocity task is adapted from [KaydenKnapik/BoosterT1mjlab](https://github.com/KaydenKnapik/BoosterT1mjlab), Apache-2.0, and has been substantially modified for this repository layout and current MJLab APIs.
+- K1 MJLab asset/configuration work references [NishB17/MJLabModified](https://github.com/NishB17/MJLabModified), Apache-2.0. The competition-compatible K1 MJCF lineage also references [RCSSServerMJ](https://github.com/YiranWang2004/RCSSServerMJ), MIT.
+- The shared AMP implementation was reorganized for this fork. [ccrpRepo/AMP_mjlab](https://github.com/ccrpRepo/AMP_mjlab) was consulted for high-level directory organization only; no source code or motion files were copied from it because the repository does not currently publish a root license.
+- Motion retargeting is compatible with [YanjieZe/GMR](https://github.com/YanjieZe/GMR), MIT. GMR's software license does not replace the license of the source motion dataset.
+
+See [NOTICE](NOTICE) and [THIRD_PARTY.md](THIRD_PARTY.md) for details.
-On a single NVIDIA 5090, the Go1 getup task converges in ~2 minutes and T1 in ~8 minutes, but we continue training with a curriculum that progressively tightens action rate, joint velocity, and power penalties to produce smoother, safer policies.
+## Checkpoints
-
-
+Checkpoint files are intentionally ignored by Git. Publish reviewed weights as versioned GitHub Release assets instead of committing them to repository history. Each released checkpoint should identify the task ID, repository commit, complete training configuration, motion-data provenance, training command, observation/action contract and weight license. See [docs/checkpoints.md](docs/checkpoints.md).
## Citation
-If you use this repository in your research, consider citing mjlab:
+If you use this repository in research, consider citing MJLab:
```bibtex
@misc{zakka2026mjlablightweightframeworkgpuaccelerated,
@@ -55,4 +123,4 @@ If you use this repository in your research, consider citing mjlab:
## License
-This repository is released under an [Apache-2.0 License](LICENSE).
+Code in this repository is released under the [Apache-2.0 License](LICENSE), except where a bundled third-party component is identified with its own license. Motion datasets and locally generated retargeted files are not covered by this repository's Apache-2.0 license.
diff --git a/THIRD_PARTY.md b/THIRD_PARTY.md
new file mode 100644
index 0000000..98c2c4e
--- /dev/null
+++ b/THIRD_PARTY.md
@@ -0,0 +1,12 @@
+# Third-party components
+
+| Component | Use in this repository | License |
+|---|---|---|
+| [mujocolab/mjlab_playground](https://github.com/mujocolab/mjlab_playground) | Base repository, get-up tasks, package/CLI style | Apache-2.0 |
+| [KaydenKnapik/BoosterT1mjlab](https://github.com/KaydenKnapik/BoosterT1mjlab) | Starting point for the T1 velocity task; adapted and modified | Apache-2.0 |
+| [NishB17/MJLabModified](https://github.com/NishB17/MJLabModified) | Reference/source lineage for Booster K1 MJLab assets and configuration | Apache-2.0 |
+| [RCSSServerMJ](https://github.com/YiranWang2004/RCSSServerMJ) | Competition-compatible Booster K1 MJCF lineage | MIT; bundled text in `third_party_licenses/RCSSServerMJ-MIT.txt` |
+| [YanjieZe/GMR](https://github.com/YanjieZe/GMR) | Recommended local motion-retargeting tool; not vendored | MIT |
+| [ccrpRepo/AMP_mjlab](https://github.com/ccrpRepo/AMP_mjlab) | Directory organization consulted only; no code or data copied | No root license found as of 2026-08-28 |
+
+The licenses above apply to software and assets supplied by those projects. They do not grant rights to separately downloaded motion datasets. See [docs/motion_data.md](docs/motion_data.md).
diff --git a/docs/checkpoints.md b/docs/checkpoints.md
new file mode 100644
index 0000000..0c72eab
--- /dev/null
+++ b/docs/checkpoints.md
@@ -0,0 +1,40 @@
+# Checkpoint releases
+
+Model checkpoints may be released separately from the source repository after a provenance and reproducibility review. The `.gitignore` excludes `*.pt` and `*.onnx`, so accidental weight commits—and another large-file push failure—are less likely.
+
+## Release layout
+
+Use a versioned GitHub Release rather than storing binary weights in Git history:
+
+```text
+-v/
+├── model.pt
+├── policy.onnx # optional deployment export
+├── env_cfg.yaml
+├── agent_cfg.yaml
+├── model_card.md
+└── SHA256SUMS
+```
+
+The release tag should point at the exact source commit used for training.
+
+## Required model-card fields
+
+Every checkpoint release should state:
+
+1. Task ID and robot model/version.
+2. Source commit and clean/dirty training-tree state.
+3. Exact `uv run train ...` command and random seed.
+4. Environment and agent configuration, including any command curriculum.
+5. Observation names/order, action names/order, control frequency and action scaling.
+6. Motion data: dataset, clip IDs, license URLs, retargeting tool/commit and whether the data itself is redistributed.
+7. Training duration, hardware and headline evaluation metrics.
+8. Known limitations and the tested deployment/runtime contract.
+9. License applied to the checkpoint and any attribution requirements.
+10. SHA-256 hashes for every binary artifact.
+
+## Motion-trained weights
+
+A checkpoint does not contain the original `.npz` or `.pkl` file, but it is still an output of a motion-trained pipeline. Before releasing one, review the source dataset's terms for restrictions on trained models, derived outputs, commercial use and attribution. If the terms are unclear, release the source/configuration first and keep the checkpoint private until permission is confirmed.
+
+Do not claim that the repository's Apache-2.0 license automatically covers a checkpoint trained from third-party data. State the checkpoint license explicitly in its release notes.
diff --git a/docs/media/t1_velocity_tracking.gif b/docs/media/t1_velocity_tracking.gif
new file mode 100644
index 0000000..421cf98
Binary files /dev/null and b/docs/media/t1_velocity_tracking.gif differ
diff --git a/docs/motion_data.md b/docs/motion_data.md
new file mode 100644
index 0000000..5f71ed1
--- /dev/null
+++ b/docs/motion_data.md
@@ -0,0 +1,91 @@
+# Motion data
+
+Motion data is deliberately kept outside Git. Both `.npz` tracking files and GMR-style `.pkl`/`.pickle` AMP files are ignored so a local licensed dataset cannot be committed accidentally.
+
+## Why retargeted files are not committed
+
+Retargeting changes a motion's robot representation; it does not automatically erase the source dataset's copyright or license conditions.
+
+- [LAFAN1](https://github.com/ubisoft/ubisoft-laforge-animation-dataset/blob/master/LICENSE) is distributed under CC BY-NC-ND 4.0. Retargeting is reasonably treated as creating adapted material, which that license does not permit users to share.
+- [AMASS](https://amass.is.tue.mpg.de/license.html) access is subject to its own terms, and AMASS aggregates datasets with additional source-specific conditions. BMLrub-derived clips should therefore not be published here without explicit redistribution permission.
+- [GMR](https://github.com/YanjieZe/GMR) is MIT-licensed software, but its license covers the retargeting code—not the motion inputs or the resulting adapted dataset.
+
+This is a conservative repository policy, not legal advice. A motion file may be published only when its complete provenance and redistribution rights are documented.
+
+## Recommended local layout
+
+Set one root for local retargeted data:
+
+```bash
+export MJLAB_PLAYGROUND_MOTION_ROOT=/absolute/path/to/mjlab_motions
+```
+
+Arrange locomotion files as follows:
+
+```text
+$MJLAB_PLAYGROUND_MOTION_ROOT/
+└── lafan/
+ ├── k1_lafan1_fk_pkl/
+ │ ├── walk1_subject5.pkl
+ │ ├── run1_subject5.pkl
+ │ └── sprint1_subject4.pkl
+ └── booster_t1_lafan1_locomotion/
+ ├── walk....pkl
+ ├── run....pkl
+ └── sprint....pkl
+```
+
+The K1 AMP loader selects `walk*.pkl`, `run*.pkl`, and `sprint*.pkl`. The T1 loader also accepts names prefixed by `lafan1_`.
+
+The T1 locomotion directory can be overridden independently:
+
+```bash
+export MJLAB_PLAYGROUND_T1_LOCOMOTION_MOTION_DIR=/absolute/path/to/t1_locomotion_pkls
+```
+
+## AMP pickle schema
+
+Each `.pkl`/`.pickle` file must contain at least:
+
+| Key | Shape/type | Meaning |
+|---|---|---|
+| `dof_pos` | `[T, num_dof]` float array | Joint positions in the robot's configured joint order |
+| `root_pos` | `[T, 3]` float array | Root position |
+| `root_rot` | `[T, 4]` float array | Root quaternion in `xyzw` order |
+| `local_body_pos` | `[T, B, 3]` float array | GMR forward-kinematics body positions |
+| `link_body_list` | sequence of `B` names | Names corresponding to `local_body_pos` |
+| `fps` | number | Source frame rate; defaults to 30 if absent |
+
+For target-location AMP, the configured `expected_dof` is 22 for K1 and 23 for T1. The required key-body names are defined in `src/mjlab_playground/amp/amp_schema.py`.
+
+## T1 reference tracking
+
+Reference tracking uses the MJLab `.motion.npz` format expected by `MotionCommandCfg`. Point directly to a locally generated file:
+
+```bash
+export MJLAB_TRACKING_MOTION_FILE=/absolute/path/to/reference.booster_t1_23dof.motion.npz
+uv run play Mjlab-Tracking-Flat-Booster-T1
+```
+
+Without that variable, the code checks this intentionally untracked package-relative placeholder:
+
+```text
+src/mjlab_playground/asset_zoo/motions/retargeted/
+└── tracking/booster_t1_23dof/
+ └── 0026_kicking2_stageii.booster_t1_23dof.motion.npz
+```
+
+The example filename documents the expected location only; the file is not distributed.
+
+## Before publishing a motion
+
+Record all of the following in a data manifest before considering a motion artifact for release:
+
+1. Original dataset and exact clip identifier.
+2. Original dataset version and license URL.
+3. Whether derivative/adapted material may be redistributed.
+4. Retargeting tool and commit.
+5. Robot model and joint order.
+6. Any attribution, noncommercial, or share-alike requirements.
+
+If item 3 is unclear or negative, keep the file local and publish only the conversion instructions.
diff --git a/pyproject.toml b/pyproject.toml
index 3178cbd..2e81684 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,7 @@ name = "mjlab-playground"
version = "0.1.0"
description = "Extra tasks for mjlab"
readme = "README.md"
-requires-python = ">=3.13"
+requires-python = ">=3.13,<3.14"
dependencies = [
"mjlab[cu128]",
"torch>=2.7.0,<2.10.0",
@@ -14,13 +14,11 @@ requires = ["uv_build>=0.8.18,<0.9.0"]
build-backend = "uv_build"
[tool.uv.sources]
-mjlab = { git = "https://github.com/mujocolab/mjlab", rev = "f7fdb163b590e980865e59a5633ff5e1143553bd" }
+mjlab = { git = "https://github.com/mujocolab/mjlab", rev = "9298ba5691858dec9f0fa7c53c0ffb453192da67" }
warp-lang = { index = "nvidia", marker = "sys_platform != 'darwin'" }
torch = [
{ index = "pytorch-cu128", marker = "sys_platform != 'darwin'" },
]
-mujoco = { index = "mujoco" }
-mujoco-warp = { git = "https://github.com/google-deepmind/mujoco_warp", rev = "875c4caf06d71b12b2036c327e7a07ce08a78b9a" }
[[tool.uv.index]]
url = "https://pypi.org/simple"
@@ -35,15 +33,6 @@ name = "pytorch-cu128"
url = "https://download.pytorch.org/whl/cu128"
explicit = true
-[[tool.uv.index]]
-name = "mujoco"
-url = "https://py.mujoco.org"
-explicit = true
-
-# The nightly index (py.mujoco.org) only has dev builds.
-[tool.uv]
-override-dependencies = ["mujoco>=3.6.0.dev0"]
-
[tool.ruff]
indent-width = 2
src = ["src/mjlab_playground"]
@@ -54,6 +43,8 @@ select = ["E4", "E7", "E9", "F", "I", "B"]
[tool.pyright]
pythonVersion = "3.13"
+include = ["src"]
+exclude = ["tests", "typings"]
ignore = ["./typings"]
stubPath = "typings"
reportWildcardImportFromLibrary = false
diff --git a/src/mjlab_playground/__init__.py b/src/mjlab_playground/__init__.py
index d6bf697..82537b3 100644
--- a/src/mjlab_playground/__init__.py
+++ b/src/mjlab_playground/__init__.py
@@ -1,4 +1,9 @@
"""A collection of tasks built on mjlab."""
-from mjlab_playground.getup.config.go1 import * # noqa: F401, F403
-from mjlab_playground.getup.config.t1 import * # noqa: F401, F403
+from mjlab_playground.tasks.getup.config.go1 import * # noqa: F401, F403
+from mjlab_playground.tasks.getup.config.k1 import * # noqa: F401, F403
+from mjlab_playground.tasks.getup.config.t1 import * # noqa: F401, F403
+from mjlab_playground.tasks.target_location_amp.config.k1 import * # noqa: F401, F403
+from mjlab_playground.tasks.target_location_amp.config.t1 import * # noqa: F401, F403
+from mjlab_playground.tasks.tracking.config.t1 import * # noqa: F401, F403
+from mjlab_playground.tasks.velocity.config.t1 import * # noqa: F401, F403
diff --git a/src/mjlab_playground/amp/__init__.py b/src/mjlab_playground/amp/__init__.py
new file mode 100644
index 0000000..c3e86c7
--- /dev/null
+++ b/src/mjlab_playground/amp/__init__.py
@@ -0,0 +1 @@
+"""Reusable adversarial motion-prior components."""
diff --git a/src/mjlab_playground/amp/amp_schema.py b/src/mjlab_playground/amp/amp_schema.py
new file mode 100644
index 0000000..017b47b
--- /dev/null
+++ b/src/mjlab_playground/amp/amp_schema.py
@@ -0,0 +1,33 @@
+"""Shared AMP observation schema for humanoid motion priors."""
+
+from __future__ import annotations
+
+K1_AMP_KEY_BODY_NAMES: tuple[str, ...] = (
+ "left_hand_link",
+ "right_hand_link",
+ "left_foot_link",
+ "right_foot_link",
+)
+
+K1_AMP_NUM_DOFS = 22
+K1_AMP_NUM_KEY_BODIES = len(K1_AMP_KEY_BODY_NAMES)
+T1_AMP_NUM_DOFS = 23
+T1_AMP_KEY_BODY_NAMES = K1_AMP_KEY_BODY_NAMES
+T1_AMP_NUM_KEY_BODIES = len(T1_AMP_KEY_BODY_NAMES)
+
+
+def amp_obs_dim(num_dofs: int, num_key_bodies: int) -> int:
+ return (
+ int(num_dofs) # joint positions
+ + int(num_dofs) # joint velocities
+ + 1 # root height
+ + 3 # projected gravity in root frame
+ + 3 # root linear velocity in root frame
+ + 3 # root angular velocity in root frame
+ + 3 * int(num_key_bodies) # key body positions in root frame
+ + 3 * int(num_key_bodies) # key body velocities in root frame
+ )
+
+
+K1_AMP_OBS_DIM = amp_obs_dim(K1_AMP_NUM_DOFS, K1_AMP_NUM_KEY_BODIES)
+T1_AMP_OBS_DIM = amp_obs_dim(T1_AMP_NUM_DOFS, T1_AMP_NUM_KEY_BODIES)
diff --git a/src/mjlab_playground/amp/mdp/__init__.py b/src/mjlab_playground/amp/mdp/__init__.py
new file mode 100644
index 0000000..b0acb1d
--- /dev/null
+++ b/src/mjlab_playground/amp/mdp/__init__.py
@@ -0,0 +1,9 @@
+"""MDP helpers shared by adversarial motion-prior tasks."""
+
+from mjlab.envs.mdp import * # noqa: F401, F403
+
+from .actions import * # noqa: F403
+from .events import * # noqa: F403
+from .observations import * # noqa: F403
+from .rewards import * # noqa: F403
+from .terminations import * # noqa: F403
diff --git a/src/mjlab_playground/amp/mdp/actions.py b/src/mjlab_playground/amp/mdp/actions.py
new file mode 100644
index 0000000..b34c8ec
--- /dev/null
+++ b/src/mjlab_playground/amp/mdp/actions.py
@@ -0,0 +1,100 @@
+"""Action terms shared by dynamic AMP tasks."""
+
+# pyright: reportIncompatibleVariableOverride=false
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+import torch
+from mjlab.envs.mdp.actions import JointPositionAction, JointPositionActionCfg
+
+if TYPE_CHECKING:
+ from mjlab.envs import ManagerBasedRlEnv
+
+
+@dataclass(kw_only=True)
+class DelayedJointPositionActionCfg(JointPositionActionCfg):
+ """Joint position control with policy-step action application latency."""
+
+ delay_steps: int = 0
+ """Number of controller steps between commanding and applying an action."""
+
+ startup_hold_steps: int = 0
+ """Episode-start steps that hold the neutral pose before policy takeover."""
+
+ def build(self, env: ManagerBasedRlEnv) -> DelayedJointPositionAction:
+ return DelayedJointPositionAction(self, env)
+
+
+class DelayedJointPositionAction(JointPositionAction):
+ """Delay actuator targets without changing ActionManager action history.
+
+ The policy-visible current/previous actions remain the latest commanded raw
+ actions. Only the processed position target written to MuJoCo is delayed,
+ matching rcssservermj's parallel observation/action loop.
+ """
+
+ cfg: DelayedJointPositionActionCfg
+
+ def __init__(
+ self,
+ cfg: DelayedJointPositionActionCfg,
+ env: ManagerBasedRlEnv,
+ ) -> None:
+ if cfg.delay_steps < 0:
+ raise ValueError(f"delay_steps must be non-negative, got {cfg.delay_steps}.")
+ if cfg.startup_hold_steps < 0:
+ raise ValueError(
+ f"startup_hold_steps must be non-negative, got {cfg.startup_hold_steps}."
+ )
+ super().__init__(cfg=cfg, env=env)
+ self._delay_steps = int(cfg.delay_steps)
+ self._startup_hold_steps = int(cfg.startup_hold_steps)
+ neutral = self._neutral_processed_action()
+ self._applied_processed_actions = neutral.clone()
+ self._processed_action_history = [neutral.clone() for _ in range(self._delay_steps)]
+
+ @property
+ def applied_processed_action(self) -> torch.Tensor:
+ """Processed target currently written to the simulated actuators."""
+ return self._applied_processed_actions
+
+ def process_actions(self, actions: torch.Tensor) -> None:
+ if self._startup_hold_steps > 0:
+ actions = actions.clone()
+ hold_mask = self._env.episode_length_buf < self._startup_hold_steps
+ actions[hold_mask] = 0.0
+ super().process_actions(actions)
+ if self._delay_steps == 0:
+ self._applied_processed_actions = self._processed_actions
+ return
+
+ self._applied_processed_actions = self._processed_action_history.pop(0)
+ self._processed_action_history.append(self._processed_actions.clone())
+
+ def apply_actions(self) -> None:
+ encoder_bias = self._entity.data.encoder_bias[:, self._target_ids]
+ target = self._applied_processed_actions - encoder_bias
+ self._entity.set_joint_position_target(target, joint_ids=self._target_ids)
+
+ def reset(self, env_ids: torch.Tensor | slice | None = None) -> None:
+ if env_ids is None:
+ env_ids = slice(None)
+ super().reset(env_ids=env_ids)
+ neutral = self._neutral_processed_action()
+ self._processed_actions[env_ids] = neutral[env_ids]
+ self._applied_processed_actions[env_ids] = neutral[env_ids]
+ for history_entry in self._processed_action_history:
+ history_entry[env_ids] = neutral[env_ids]
+
+ def _neutral_processed_action(self) -> torch.Tensor:
+ neutral = torch.zeros_like(self._processed_actions) * self._scale + self._offset
+ if self.cfg.clip is not None:
+ neutral = torch.clamp(
+ neutral,
+ min=self._clip[:, :, 0],
+ max=self._clip[:, :, 1],
+ )
+ return neutral
diff --git a/src/mjlab_playground/amp/mdp/events.py b/src/mjlab_playground/amp/mdp/events.py
new file mode 100644
index 0000000..82cfbff
--- /dev/null
+++ b/src/mjlab_playground/amp/mdp/events.py
@@ -0,0 +1,63 @@
+"""Reset events shared by locomotion AMP tasks."""
+
+from __future__ import annotations
+
+import torch
+from mjlab.utils.lab_api.math import quat_from_euler_xyz
+
+
+def _env_ids(env, env_ids: torch.Tensor | slice | None) -> torch.Tensor:
+ all_ids = torch.arange(env.num_envs, device=env.device, dtype=torch.int64)
+ if env_ids is None:
+ return all_ids
+ if isinstance(env_ids, slice):
+ return all_ids[env_ids]
+ return env_ids
+
+
+def reset_robot_near_origin(
+ env,
+ env_ids: torch.Tensor | slice | None,
+ robot_name: str = "robot",
+ x_range: tuple[float, float] = (-0.03, 0.03),
+ y_range: tuple[float, float] = (-0.03, 0.03),
+ yaw_range: tuple[float, float] = (-0.15, 0.15),
+ z_offset: float = 0.02,
+ z_offset_range: tuple[float, float] | None = None,
+ roll_range: tuple[float, float] = (0.0, 0.0),
+ pitch_range: tuple[float, float] = (0.0, 0.0),
+ root_lin_vel_xy_range: tuple[float, float] = (0.0, 0.0),
+ root_lin_vel_z_range: tuple[float, float] = (0.0, 0.0),
+ root_ang_vel_range: tuple[float, float] = (0.0, 0.0),
+ phase_period_s: float | None = None,
+) -> None:
+ """Reset a standing robot near its environment origin with optional noise."""
+ del phase_period_s
+ resolved_ids = _env_ids(env, env_ids)
+ robot = env.scene[robot_name]
+ num_resets = len(resolved_ids)
+
+ default_root_state = robot.data.default_root_state[resolved_ids].clone()
+ positions = env.scene.env_origins[resolved_ids] + default_root_state[:, :3]
+ positions[:, 0] += torch.empty(num_resets, device=env.device).uniform_(*x_range)
+ positions[:, 1] += torch.empty(num_resets, device=env.device).uniform_(*y_range)
+ if z_offset_range is None:
+ positions[:, 2] += float(z_offset)
+ else:
+ positions[:, 2] += torch.empty(num_resets, device=env.device).uniform_(
+ *z_offset_range
+ )
+
+ roll = torch.empty(num_resets, device=env.device).uniform_(*roll_range)
+ pitch = torch.empty(num_resets, device=env.device).uniform_(*pitch_range)
+ yaw = torch.empty(num_resets, device=env.device).uniform_(*yaw_range)
+ quat = quat_from_euler_xyz(roll, pitch, yaw)
+ robot.write_root_link_pose_to_sim(
+ torch.cat((positions, quat), dim=-1), env_ids=resolved_ids
+ )
+
+ root_velocity = torch.empty(num_resets, 6, device=env.device)
+ root_velocity[:, :2].uniform_(*root_lin_vel_xy_range)
+ root_velocity[:, 2].uniform_(*root_lin_vel_z_range)
+ root_velocity[:, 3:].uniform_(*root_ang_vel_range)
+ robot.write_root_link_velocity_to_sim(root_velocity, env_ids=resolved_ids)
diff --git a/src/mjlab_playground/amp/mdp/observations.py b/src/mjlab_playground/amp/mdp/observations.py
new file mode 100644
index 0000000..332662f
--- /dev/null
+++ b/src/mjlab_playground/amp/mdp/observations.py
@@ -0,0 +1,65 @@
+"""Observations shared by adversarial motion-prior tasks."""
+
+from __future__ import annotations
+
+import torch
+from mjlab.managers.scene_entity_config import SceneEntityCfg
+from mjlab.utils.lab_api.math import quat_apply_inverse
+
+from mjlab_playground.amp.amp_schema import K1_AMP_KEY_BODY_NAMES
+
+_ROBOT_CFG = SceneEntityCfg("robot", joint_names=(".*",))
+
+
+def amp_robot_obs(
+ env,
+ robot_cfg: SceneEntityCfg = _ROBOT_CFG,
+ key_body_names: tuple[str, ...] = K1_AMP_KEY_BODY_NAMES,
+) -> torch.Tensor:
+ """Build the robot-only state consumed by the AMP discriminator."""
+ robot = env.scene[robot_cfg.name]
+ joint_ids = robot_cfg.joint_ids
+ body_ids = _resolve_key_body_ids(env, robot, key_body_names)
+ root_quat = robot.data.root_link_quat_w
+ root_pos = robot.data.root_link_pos_w
+ root_quat_bodies = root_quat[:, None, :].expand(-1, len(body_ids), -1)
+
+ body_pos_w = robot.data.body_link_pos_w[:, body_ids, :] - root_pos[:, None, :]
+ body_vel_w = (
+ robot.data.body_link_lin_vel_w[:, body_ids, :]
+ - robot.data.root_link_lin_vel_w[:, None, :]
+ )
+ key_body_pos_b_raw = quat_apply_inverse(root_quat_bodies, body_pos_w)
+ key_body_vel_b_raw = quat_apply_inverse(root_quat_bodies, body_vel_w)
+ key_body_vel_b_raw = key_body_vel_b_raw - torch.cross(
+ robot.data.root_link_ang_vel_b[:, None, :],
+ key_body_pos_b_raw,
+ dim=-1,
+ )
+
+ return torch.cat(
+ (
+ robot.data.joint_pos[:, joint_ids],
+ robot.data.joint_vel[:, joint_ids],
+ root_pos[:, 2:3],
+ robot.data.projected_gravity_b,
+ robot.data.root_link_lin_vel_b,
+ robot.data.root_link_ang_vel_b,
+ key_body_pos_b_raw.reshape(env.num_envs, -1),
+ key_body_vel_b_raw.reshape(env.num_envs, -1),
+ ),
+ dim=-1,
+ )
+
+
+def _resolve_key_body_ids(env, robot, key_body_names: tuple[str, ...]) -> list[int]:
+ cached_ids = getattr(env, "_amp_key_body_ids", None)
+ cached_names = getattr(env, "_amp_key_body_names", None)
+ if cached_ids is not None and cached_names == key_body_names:
+ return cached_ids
+ body_ids = robot.find_bodies(key_body_names, preserve_order=True)[0]
+ if len(body_ids) != len(key_body_names):
+ raise ValueError(f"Failed to resolve AMP key bodies: {key_body_names}.")
+ env._amp_key_body_ids = body_ids
+ env._amp_key_body_names = key_body_names
+ return body_ids
diff --git a/src/mjlab_playground/amp/mdp/rewards.py b/src/mjlab_playground/amp/mdp/rewards.py
new file mode 100644
index 0000000..aedd745
--- /dev/null
+++ b/src/mjlab_playground/amp/mdp/rewards.py
@@ -0,0 +1,47 @@
+"""Reward and metric helpers shared by locomotion AMP tasks."""
+
+# pyright: reportOptionalMemberAccess=false
+
+from __future__ import annotations
+
+import torch
+
+from .terminations import fell_over as termination_fell_over
+
+
+def fell_over_penalty(
+ env,
+ robot_name: str = "robot",
+ height_threshold: float = 0.38,
+ up_z_threshold: float = 0.55,
+) -> torch.Tensor:
+ """Return a fall indicator aligned with the current-step termination."""
+ termination_manager = getattr(env, "termination_manager", None)
+ try:
+ fell = termination_manager.get_term("fell_over")
+ except (AttributeError, KeyError):
+ fell = termination_fell_over(
+ env,
+ robot_name=robot_name,
+ height_threshold=height_threshold,
+ up_z_threshold=up_z_threshold,
+ )
+ env.extras.setdefault("log", {})["Metrics/robot/fall_fraction"] = fell.float().mean()
+ if bool(getattr(env.cfg, "scale_rewards_by_dt", True)):
+ return fell.float() / max(float(env.step_dt), 1.0e-6)
+ return fell.float()
+
+
+def fell_over_fraction(
+ env,
+ robot_name: str = "robot",
+ height_threshold: float = 0.38,
+ up_z_threshold: float = 0.55,
+) -> torch.Tensor:
+ """Per-step fall indicator for episode metrics."""
+ return termination_fell_over(
+ env,
+ robot_name=robot_name,
+ height_threshold=height_threshold,
+ up_z_threshold=up_z_threshold,
+ ).float()
diff --git a/src/mjlab_playground/amp/mdp/terminations.py b/src/mjlab_playground/amp/mdp/terminations.py
new file mode 100644
index 0000000..cbc50bb
--- /dev/null
+++ b/src/mjlab_playground/amp/mdp/terminations.py
@@ -0,0 +1,18 @@
+"""Termination helpers shared by locomotion AMP tasks."""
+
+from __future__ import annotations
+
+import torch
+
+
+def fell_over(
+ env,
+ robot_name: str = "robot",
+ height_threshold: float = 0.38,
+ up_z_threshold: float = 0.55,
+) -> torch.Tensor:
+ """Return environments whose robot is too low or too tilted."""
+ robot = env.scene[robot_name]
+ height = robot.data.root_link_pos_w[:, 2]
+ up_z = -robot.data.projected_gravity_b[:, 2]
+ return (height < height_threshold) | (up_z < up_z_threshold)
diff --git a/src/mjlab_playground/amp/rl/__init__.py b/src/mjlab_playground/amp/rl/__init__.py
new file mode 100644
index 0000000..7d6c3fd
--- /dev/null
+++ b/src/mjlab_playground/amp/rl/__init__.py
@@ -0,0 +1,6 @@
+"""Reusable adversarial motion-prior training components."""
+
+from .amp_ppo import AmpPPO
+from .runner import AmpOnPolicyRunner
+
+__all__ = ["AmpPPO", "AmpOnPolicyRunner"]
diff --git a/src/mjlab_playground/amp/rl/amp_ppo.py b/src/mjlab_playground/amp/rl/amp_ppo.py
new file mode 100644
index 0000000..69eba79
--- /dev/null
+++ b/src/mjlab_playground/amp/rl/amp_ppo.py
@@ -0,0 +1,788 @@
+"""PPO variant with an AMP discriminator update."""
+
+# pyright: reportArgumentType=false, reportCallIssue=false, reportIndexIssue=false
+# pyright: reportOptionalMemberAccess=false, reportPossiblyUnboundVariable=false
+
+from __future__ import annotations
+
+from itertools import chain
+
+import torch
+import torch.nn as nn
+from rsl_rl.algorithms.ppo import PPO
+from rsl_rl.env import VecEnv
+from rsl_rl.extensions import resolve_rnd_config, resolve_symmetry_config
+from rsl_rl.models import MLPModel
+from rsl_rl.storage import RolloutStorage
+from rsl_rl.utils import resolve_callable, resolve_obs_groups, resolve_optimizer
+from tensordict import TensorDict
+
+from .discriminator import AmpDiscriminator
+from .motion_loader import RetargetedAmpMotionLoader
+from .replay_buffer import AmpTransitionBuffer
+
+
+class AmpPPO(PPO):
+ """Current RSL-RL-compatible PPO with AMP discriminator training."""
+
+ def __init__(
+ self,
+ actor: MLPModel,
+ critic: MLPModel,
+ storage: RolloutStorage,
+ discriminator: AmpDiscriminator,
+ expert_loader: RetargetedAmpMotionLoader,
+ replay_buffer: AmpTransitionBuffer,
+ stage_discriminators: dict[str, AmpDiscriminator] | None = None,
+ stage_expert_loaders: dict[str, RetargetedAmpMotionLoader] | None = None,
+ stage_replay_buffers: dict[str, AmpTransitionBuffer] | None = None,
+ amp_loss_coef: float = 1.0,
+ amp_cfg: dict | None = None,
+ num_learning_epochs: int = 5,
+ num_mini_batches: int = 4,
+ clip_param: float = 0.2,
+ gamma: float = 0.99,
+ lam: float = 0.95,
+ value_loss_coef: float = 1.0,
+ entropy_coef: float = 0.01,
+ learning_rate: float = 0.001,
+ discriminator_learning_rate: float | None = None,
+ discriminator_weight_decay: float = 1.0e-4,
+ max_grad_norm: float = 1.0,
+ optimizer: str = "adam",
+ use_clipped_value_loss: bool = True,
+ schedule: str = "adaptive",
+ desired_kl: float = 0.01,
+ normalize_advantage_per_mini_batch: bool = False,
+ device: str = "cpu",
+ rnd_cfg: dict | None = None,
+ symmetry_cfg: dict | None = None,
+ multi_gpu_cfg: dict | None = None,
+ ) -> None:
+ super().__init__(
+ actor=actor,
+ critic=critic,
+ storage=storage,
+ num_learning_epochs=num_learning_epochs,
+ num_mini_batches=num_mini_batches,
+ clip_param=clip_param,
+ gamma=gamma,
+ lam=lam,
+ value_loss_coef=value_loss_coef,
+ entropy_coef=entropy_coef,
+ learning_rate=learning_rate,
+ max_grad_norm=max_grad_norm,
+ optimizer=optimizer,
+ use_clipped_value_loss=use_clipped_value_loss,
+ schedule=schedule,
+ desired_kl=desired_kl,
+ normalize_advantage_per_mini_batch=normalize_advantage_per_mini_batch,
+ device=device,
+ rnd_cfg=rnd_cfg,
+ symmetry_cfg=symmetry_cfg,
+ multi_gpu_cfg=multi_gpu_cfg,
+ )
+ self.discriminator = discriminator.to(self.device)
+ self.expert_loader = expert_loader
+ self.amp_replay = replay_buffer
+ self.amp_cfg = dict(amp_cfg or {})
+ self.stage_discriminators = {
+ name: module.to(self.device)
+ for name, module in (stage_discriminators or {}).items()
+ }
+ self.stage_expert_loaders = dict(stage_expert_loaders or {})
+ self.stage_replay_buffers = dict(stage_replay_buffers or {})
+ self.uses_stage_amp = bool(self.stage_discriminators)
+ self.stage_policy_replay: AmpTransitionBuffer | None = None
+ if self.uses_stage_amp:
+ self.stage_policy_replay = AmpTransitionBuffer(
+ capacity=replay_buffer.capacity,
+ amp_obs_dim=replay_buffer.amp_obs_dim,
+ device=device,
+ )
+ self.task_reward_lerp = float(self.discriminator.task_reward_lerp)
+ self.amp_loss_coef = float(amp_loss_coef)
+ self.discriminator_learning_rate = (
+ float(learning_rate)
+ if discriminator_learning_rate is None
+ else float(discriminator_learning_rate)
+ )
+
+ self.optimizer = resolve_optimizer(optimizer)(
+ [
+ {
+ "params": chain(self.actor.parameters(), self.critic.parameters()),
+ "name": "policy",
+ "lr": learning_rate,
+ },
+ {
+ "params": list(self._discriminator_parameters()),
+ "name": "discriminator",
+ "lr": self.discriminator_learning_rate,
+ "weight_decay": float(discriminator_weight_decay),
+ },
+ ],
+ lr=learning_rate,
+ )
+
+ def _discriminator_modules(self) -> list[AmpDiscriminator]:
+ if self.uses_stage_amp:
+ return list(self.stage_discriminators.values())
+ return [self.discriminator]
+
+ def _discriminator_parameters(self):
+ return chain.from_iterable(
+ module.parameters() for module in self._discriminator_modules()
+ )
+
+ @staticmethod
+ def construct_algorithm(
+ obs: TensorDict, env: VecEnv, cfg: dict, device: str
+ ) -> "AmpPPO":
+ alg_class: type[AmpPPO] = resolve_callable(cfg["algorithm"].pop("class_name")) # type: ignore
+ actor_class: type[MLPModel] = resolve_callable(cfg["actor"].pop("class_name")) # type: ignore
+ critic_class: type[MLPModel] = resolve_callable(cfg["critic"].pop("class_name")) # type: ignore
+
+ cfg["obs_groups"] = resolve_obs_groups(obs, cfg["obs_groups"], ["actor", "critic"])
+ cfg["algorithm"] = resolve_rnd_config(cfg["algorithm"], obs, cfg["obs_groups"], env)
+ cfg["algorithm"] = resolve_symmetry_config(cfg["algorithm"], env)
+
+ amp_cfg = cfg["algorithm"].pop("amp_cfg")
+ actor: MLPModel = actor_class(
+ obs,
+ cfg["obs_groups"],
+ "actor",
+ env.num_actions,
+ **cfg["actor"],
+ ).to(device)
+ print(f"Actor Model: {actor}")
+ if cfg["algorithm"].pop("share_cnn_encoders", None):
+ cfg["critic"]["cnns"] = actor.cnns # type: ignore[attr-defined]
+ critic: MLPModel = critic_class(
+ obs, cfg["obs_groups"], "critic", 1, **cfg["critic"]
+ ).to(device)
+ print(f"Critic Model: {critic}")
+
+ storage = RolloutStorage(
+ "rl",
+ env.num_envs,
+ cfg["num_steps_per_env"],
+ obs,
+ [env.num_actions],
+ device,
+ )
+
+ def make_loader(local_cfg: dict) -> RetargetedAmpMotionLoader:
+ return RetargetedAmpMotionLoader(
+ tuple(local_cfg.get("motion_files", ())),
+ device=device,
+ expected_dof=local_cfg.get("expected_dof", amp_cfg.get("expected_dof", 22)),
+ motion_groups=local_cfg.get("motion_groups"),
+ target_fps=local_cfg.get("target_fps", amp_cfg.get("target_fps")),
+ joint_names=tuple(local_cfg.get("joint_names", amp_cfg.get("joint_names", ())))
+ or None,
+ key_body_names=tuple(
+ local_cfg.get("key_body_names", amp_cfg.get("key_body_names", ()))
+ )
+ or None,
+ emphasis_frame_offset_range=local_cfg.get(
+ "emphasis_frame_offset_range",
+ amp_cfg.get("emphasis_frame_offset_range"),
+ ),
+ emphasis_weight=local_cfg.get(
+ "emphasis_weight",
+ amp_cfg.get("emphasis_weight"),
+ ),
+ velocity_emphasis_weight=local_cfg.get(
+ "velocity_emphasis_weight",
+ amp_cfg.get("velocity_emphasis_weight"),
+ ),
+ )
+
+ def make_discriminator(
+ loader: RetargetedAmpMotionLoader,
+ local_cfg: dict,
+ ) -> AmpDiscriminator:
+ return AmpDiscriminator(
+ amp_obs_dim=loader.amp_obs_dim,
+ hidden_dims=tuple(
+ local_cfg.get(
+ "discriminator_hidden_dims",
+ amp_cfg.get("discriminator_hidden_dims", (512, 256)),
+ )
+ ),
+ activation=local_cfg.get(
+ "discriminator_activation",
+ amp_cfg.get("discriminator_activation", "relu"),
+ ),
+ amp_reward_coef=local_cfg.get("reward_coef", amp_cfg.get("reward_coef", 0.3)),
+ amp_reward_scale=local_cfg.get(
+ "reward_scale", amp_cfg.get("reward_scale", 1.0)
+ ),
+ task_reward_lerp=amp_cfg.get("task_reward_lerp", 0.7),
+ grad_penalty_coef=local_cfg.get(
+ "grad_penalty_coef",
+ amp_cfg.get("grad_penalty_coef", 10.0),
+ ),
+ normalize_input=local_cfg.get(
+ "normalize_input", amp_cfg.get("normalize_input", True)
+ ),
+ loss_type=local_cfg.get(
+ "discriminator_loss_type",
+ amp_cfg.get("discriminator_loss_type", "bce"),
+ ),
+ reward_type=local_cfg.get(
+ "discriminator_reward_type",
+ amp_cfg.get("discriminator_reward_type", "softplus"),
+ ),
+ expert_label=local_cfg.get(
+ "discriminator_expert_label",
+ amp_cfg.get("discriminator_expert_label", 0.9),
+ ),
+ policy_label=local_cfg.get(
+ "discriminator_policy_label",
+ amp_cfg.get("discriminator_policy_label", 0.1),
+ ),
+ reward_clip_max=local_cfg.get(
+ "discriminator_reward_clip_max",
+ amp_cfg.get("discriminator_reward_clip_max", 1.5),
+ ),
+ ).to(device)
+
+ stage_cfg = dict(amp_cfg.get("stage_amp_cfg", {}))
+ stage_discriminators: dict[str, AmpDiscriminator] = {}
+ stage_expert_loaders: dict[str, RetargetedAmpMotionLoader] = {}
+ stage_replay_buffers: dict[str, AmpTransitionBuffer] = {}
+ if stage_cfg.get("enabled", False):
+ reserved_stage_cfg_keys = {"enabled", "stage_names", "stages", "primary_stage"}
+ if isinstance(stage_cfg.get("stages"), dict):
+ stage_items = list(stage_cfg["stages"].items())
+ else:
+ stage_names = stage_cfg.get("stage_names")
+ if stage_names is None:
+ inferred_names = [
+ name for name in stage_cfg.keys() if name not in reserved_stage_cfg_keys
+ ]
+ if inferred_names:
+ stage_names = tuple(inferred_names)
+ else:
+ raise ValueError(
+ "stage_amp_cfg is enabled but no stage definitions were provided."
+ )
+ stage_items = [
+ (str(name), stage_cfg.get(str(name), {})) for name in stage_names
+ ]
+
+ if not stage_items:
+ raise ValueError("stage_amp_cfg is enabled but resolved to zero AMP stages.")
+
+ for name, raw_local_cfg in stage_items:
+ local_cfg = dict(raw_local_cfg)
+ local_cfg.setdefault("motion_groups", stage_cfg.get(f"{name}_motion_groups"))
+ local_cfg.setdefault("motion_files", stage_cfg.get(f"{name}_motion_files", ()))
+ loader = make_loader(local_cfg)
+ stage_expert_loaders[name] = loader
+ stage_discriminators[name] = make_discriminator(loader, local_cfg)
+ stage_replay_buffers[name] = AmpTransitionBuffer(
+ capacity=local_cfg.get(
+ "replay_buffer_size", amp_cfg.get("replay_buffer_size", 100_000)
+ ),
+ amp_obs_dim=loader.amp_obs_dim,
+ device=device,
+ )
+ primary_stage_name = str(stage_cfg.get("primary_stage", ""))
+ if primary_stage_name not in stage_discriminators:
+ primary_stage_name = next(iter(stage_discriminators))
+ expert_loader = stage_expert_loaders[primary_stage_name]
+ discriminator = stage_discriminators[primary_stage_name]
+ replay_buffer = stage_replay_buffers[primary_stage_name]
+ for name, loader in stage_expert_loaders.items():
+ print(
+ f"AMP {name} expert transitions: "
+ f"{loader.num_transitions}, obs dim: {loader.amp_obs_dim}"
+ )
+ else:
+ expert_loader = make_loader(amp_cfg)
+ discriminator = make_discriminator(expert_loader, amp_cfg)
+ replay_buffer = AmpTransitionBuffer(
+ capacity=amp_cfg.get("replay_buffer_size", 100_000),
+ amp_obs_dim=expert_loader.amp_obs_dim,
+ device=device,
+ )
+ print(
+ "AMP expert transitions: "
+ f"{expert_loader.num_transitions}, obs dim: {expert_loader.amp_obs_dim}"
+ )
+
+ return alg_class(
+ actor=actor,
+ critic=critic,
+ storage=storage,
+ discriminator=discriminator,
+ expert_loader=expert_loader,
+ replay_buffer=replay_buffer,
+ stage_discriminators=stage_discriminators or None,
+ stage_expert_loaders=stage_expert_loaders or None,
+ stage_replay_buffers=stage_replay_buffers or None,
+ amp_loss_coef=amp_cfg.get("loss_coef", 1.0),
+ amp_cfg=amp_cfg,
+ discriminator_learning_rate=amp_cfg.get("discriminator_learning_rate"),
+ discriminator_weight_decay=amp_cfg.get("discriminator_weight_decay", 1.0e-4),
+ device=device,
+ **cfg["algorithm"],
+ multi_gpu_cfg=cfg["multi_gpu"],
+ )
+
+ def train_mode(self) -> None:
+ super().train_mode()
+ for discriminator in self._discriminator_modules():
+ discriminator.train()
+
+ def eval_mode(self) -> None:
+ super().eval_mode()
+ for discriminator in self._discriminator_modules():
+ discriminator.eval()
+
+ def predict_amp_reward(
+ self,
+ state: torch.Tensor,
+ next_state: torch.Tensor,
+ task_reward: torch.Tensor,
+ stage_weights: dict[str, torch.Tensor] | None = None,
+ ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, dict[str, torch.Tensor]]:
+ """Return mixed reward, raw style reward, discriminator score, and stage logs."""
+ if not self.uses_stage_amp:
+ mixed, disc = self.discriminator.predict_reward(state, next_state, task_reward)
+ amp_weight = 1.0 - self.task_reward_lerp
+ style_reward = torch.zeros_like(task_reward)
+ if amp_weight > 0.0:
+ style_reward = (mixed - self.task_reward_lerp * task_reward) / amp_weight
+ return mixed, style_reward, disc, {}
+
+ if stage_weights is None:
+ stage_weights = {
+ name: torch.full_like(task_reward, 1.0 / len(self.stage_discriminators))
+ for name in self.stage_discriminators
+ }
+ total_weight = torch.zeros_like(task_reward)
+ style_reward = torch.zeros_like(task_reward)
+ disc_pred = torch.zeros_like(task_reward)
+ logs: dict[str, torch.Tensor] = {}
+ for name, discriminator in self.stage_discriminators.items():
+ weight = stage_weights.get(name)
+ if weight is None:
+ weight = torch.zeros_like(task_reward)
+ weight = weight.to(device=task_reward.device, dtype=task_reward.dtype).view(-1)
+ stage_style, stage_disc = discriminator.predict_style_reward(state, next_state)
+ style_reward += weight * stage_style
+ disc_pred += weight * stage_disc
+ total_weight += weight
+ logs[f"{name}_weight"] = weight.detach()
+ logs[f"{name}_style"] = stage_style.detach()
+ logs[f"{name}_disc"] = stage_disc.detach()
+ safe_weight = total_weight.clamp_min(1.0e-6)
+ style_reward = style_reward / safe_weight
+ disc_pred = disc_pred / safe_weight
+ mixed = (
+ self.task_reward_lerp * task_reward + (1.0 - self.task_reward_lerp) * style_reward
+ )
+ return mixed, style_reward, disc_pred, logs
+
+ def process_amp_step(
+ self,
+ state: torch.Tensor,
+ next_state: torch.Tensor,
+ stage_weights: dict[str, torch.Tensor] | None = None,
+ ) -> None:
+ if not self.uses_stage_amp:
+ self.amp_replay.insert(state, next_state)
+ return
+ if self.stage_policy_replay is not None:
+ self.stage_policy_replay.insert(state, next_state)
+ if stage_weights is None:
+ return
+ for name, replay_buffer in self.stage_replay_buffers.items():
+ weight = stage_weights.get(name)
+ if weight is None:
+ continue
+ mask = weight.to(device=state.device).view(-1) >= 0.5
+ if torch.any(mask):
+ replay_buffer.insert(state[mask], next_state[mask])
+
+ def _sample_stage_policy(
+ self,
+ name: str,
+ batch_size: int,
+ ) -> tuple[torch.Tensor, torch.Tensor, bool]:
+ replay = self.stage_replay_buffers[name]
+ if replay.size > 0:
+ policy_state, policy_next_state = replay.sample(batch_size)
+ return policy_state, policy_next_state, False
+ if self.stage_policy_replay is not None and self.stage_policy_replay.size > 0:
+ policy_state, policy_next_state = self.stage_policy_replay.sample(batch_size)
+ return policy_state, policy_next_state, True
+ raise RuntimeError(
+ f"Stage AMP replay buffer {name!r} is empty and no policy fallback "
+ "transitions are available. This means no policy AMP transitions were "
+ "recorded before the discriminator update."
+ )
+
+ def update(self) -> dict[str, float]: # noqa: C901
+ mean_value_loss = 0.0
+ mean_surrogate_loss = 0.0
+ mean_entropy = 0.0
+ mean_amp_loss = 0.0
+ mean_grad_pen = 0.0
+ mean_policy_pred = 0.0
+ mean_expert_pred = 0.0
+ mean_stage_stats = {
+ name: {
+ "amp": 0.0,
+ "policy_pred": 0.0,
+ "expert_pred": 0.0,
+ "fallback": 0.0,
+ }
+ for name in self.stage_discriminators
+ }
+ mean_rnd_loss = 0.0 if self.rnd else None
+ mean_symmetry_loss = 0.0 if self.symmetry else None
+
+ if self.actor.is_recurrent or self.critic.is_recurrent:
+ generator = self.storage.recurrent_mini_batch_generator(
+ self.num_mini_batches,
+ self.num_learning_epochs,
+ )
+ else:
+ generator = self.storage.mini_batch_generator(
+ self.num_mini_batches,
+ self.num_learning_epochs,
+ )
+
+ for batch in generator:
+ original_batch_size = batch.observations.batch_size[0]
+
+ if self.normalize_advantage_per_mini_batch:
+ with torch.no_grad():
+ batch.advantages = (batch.advantages - batch.advantages.mean()) / ( # type: ignore[union-attr]
+ batch.advantages.std() + 1e-8 # type: ignore[union-attr]
+ )
+
+ if self.symmetry and self.symmetry["use_data_augmentation"]:
+ data_augmentation_func = self.symmetry["data_augmentation_func"]
+ batch.observations, batch.actions = data_augmentation_func(
+ env=self.symmetry["_env"],
+ obs=batch.observations,
+ actions=batch.actions,
+ )
+ num_aug = int(batch.observations.batch_size[0] / original_batch_size)
+ batch.old_actions_log_prob = batch.old_actions_log_prob.repeat(num_aug, 1)
+ batch.values = batch.values.repeat(num_aug, 1)
+ batch.advantages = batch.advantages.repeat(num_aug, 1)
+ batch.returns = batch.returns.repeat(num_aug, 1)
+
+ self.actor(
+ batch.observations,
+ masks=batch.masks,
+ hidden_state=batch.hidden_states[0],
+ stochastic_output=True,
+ )
+ actions_log_prob = self.actor.get_output_log_prob(batch.actions) # type: ignore[arg-type]
+ values = self.critic(
+ batch.observations,
+ masks=batch.masks,
+ hidden_state=batch.hidden_states[1],
+ )
+ distribution_params = tuple(
+ p[:original_batch_size] for p in self.actor.output_distribution_params
+ )
+ entropy = self.actor.output_entropy[:original_batch_size]
+
+ if self.desired_kl is not None and self.schedule == "adaptive":
+ with torch.inference_mode():
+ kl = self.actor.get_kl_divergence(
+ batch.old_distribution_params,
+ distribution_params,
+ )
+ kl_mean = torch.mean(kl)
+ if self.is_multi_gpu:
+ torch.distributed.all_reduce(kl_mean, op=torch.distributed.ReduceOp.SUM)
+ kl_mean /= self.gpu_world_size
+ if self.gpu_global_rank == 0:
+ if kl_mean > self.desired_kl * 2.0:
+ self.learning_rate = max(1.0e-5, self.learning_rate / 1.5)
+ elif kl_mean < self.desired_kl / 2.0 and kl_mean > 0.0:
+ self.learning_rate = min(1.0e-2, self.learning_rate * 1.5)
+ if self.is_multi_gpu:
+ lr_tensor = torch.tensor(self.learning_rate, device=self.device)
+ torch.distributed.broadcast(lr_tensor, src=0)
+ self.learning_rate = lr_tensor.item()
+ for param_group in self.optimizer.param_groups:
+ if param_group.get("name") == "discriminator":
+ param_group["lr"] = self.discriminator_learning_rate
+ else:
+ param_group["lr"] = self.learning_rate
+
+ ratio = torch.exp(actions_log_prob - torch.squeeze(batch.old_actions_log_prob)) # type: ignore[arg-type]
+ surrogate = -torch.squeeze(batch.advantages) * ratio # type: ignore[arg-type]
+ surrogate_clipped = -torch.squeeze(batch.advantages) * torch.clamp( # type: ignore[arg-type]
+ ratio,
+ 1.0 - self.clip_param,
+ 1.0 + self.clip_param,
+ )
+ surrogate_loss = torch.max(surrogate, surrogate_clipped).mean()
+
+ if self.use_clipped_value_loss:
+ value_clipped = batch.values + (values - batch.values).clamp( # type: ignore[operator]
+ -self.clip_param,
+ self.clip_param,
+ )
+ value_losses = (values - batch.returns).pow(2)
+ value_losses_clipped = (value_clipped - batch.returns).pow(2)
+ value_loss = torch.max(value_losses, value_losses_clipped).mean()
+ else:
+ value_loss = (batch.returns - values).pow(2).mean()
+
+ loss = (
+ surrogate_loss
+ + self.value_loss_coef * value_loss
+ - self.entropy_coef * entropy.mean()
+ )
+
+ if self.symmetry:
+ if not self.symmetry["use_data_augmentation"]:
+ data_augmentation_func = self.symmetry["data_augmentation_func"]
+ batch.observations, _ = data_augmentation_func(
+ obs=batch.observations,
+ actions=None,
+ env=self.symmetry["_env"],
+ )
+ mean_actions = self.actor(batch.observations.detach().clone())
+ action_mean_orig = mean_actions[:original_batch_size]
+ _, actions_mean_symm = data_augmentation_func(
+ obs=None,
+ actions=action_mean_orig,
+ env=self.symmetry["_env"],
+ )
+ mse_loss = torch.nn.MSELoss()
+ symmetry_loss = mse_loss(
+ mean_actions[original_batch_size:],
+ actions_mean_symm.detach()[original_batch_size:],
+ )
+ if self.symmetry["use_mirror_loss"]:
+ loss += self.symmetry["mirror_loss_coeff"] * symmetry_loss
+ else:
+ symmetry_loss = symmetry_loss.detach()
+
+ rnd_loss = (
+ self.rnd.compute_loss(batch.observations[:original_batch_size]) # type: ignore[index]
+ if self.rnd
+ else None
+ )
+
+ mini_batch_size = int(original_batch_size)
+ if self.uses_stage_amp:
+ amp_loss = torch.zeros((), dtype=torch.float32, device=self.device)
+ stage_stats: list[dict[str, torch.Tensor]] = []
+ for name, discriminator in self.stage_discriminators.items():
+ loader = self.stage_expert_loaders[name]
+ policy_state, policy_next_state, used_fallback = self._sample_stage_policy(
+ name,
+ mini_batch_size,
+ )
+ expert_state, expert_next_state = loader.sample(mini_batch_size)
+ stage_loss, stats = discriminator.discriminator_loss(
+ policy_state,
+ policy_next_state,
+ expert_state,
+ expert_next_state,
+ )
+ amp_loss = amp_loss + stage_loss
+ stage_stats.append(stats)
+ mean_stage_stats[name]["amp"] += stats["amp"].item()
+ mean_stage_stats[name]["policy_pred"] += stats["amp_policy_pred"].item()
+ mean_stage_stats[name]["expert_pred"] += stats["amp_expert_pred"].item()
+ mean_stage_stats[name]["fallback"] += float(used_fallback)
+ amp_loss = amp_loss / max(len(stage_stats), 1)
+ amp_stats = {
+ key: torch.stack([stats[key] for stats in stage_stats]).mean()
+ for key in stage_stats[0]
+ }
+ else:
+ if self.amp_replay.size > 0:
+ policy_state, policy_next_state = self.amp_replay.sample(mini_batch_size)
+ else:
+ policy_state, policy_next_state = self.expert_loader.sample(mini_batch_size)
+ expert_state, expert_next_state = self.expert_loader.sample(mini_batch_size)
+ amp_loss, amp_stats = self.discriminator.discriminator_loss(
+ policy_state,
+ policy_next_state,
+ expert_state,
+ expert_next_state,
+ )
+ loss += self.amp_loss_coef * amp_loss
+
+ self.optimizer.zero_grad()
+ loss.backward()
+ if self.rnd:
+ self.rnd.optimizer.zero_grad()
+ assert rnd_loss is not None
+ rnd_loss.backward()
+
+ if self.is_multi_gpu:
+ self.reduce_parameters()
+
+ nn.utils.clip_grad_norm_(self.actor.parameters(), self.max_grad_norm)
+ nn.utils.clip_grad_norm_(self.critic.parameters(), self.max_grad_norm)
+ nn.utils.clip_grad_norm_(
+ list(self._discriminator_parameters()), self.max_grad_norm
+ )
+ self.optimizer.step()
+ if self.rnd:
+ self.rnd.optimizer.step()
+
+ mean_value_loss += value_loss.item()
+ mean_surrogate_loss += surrogate_loss.item()
+ mean_entropy += entropy.mean().item()
+ mean_amp_loss += amp_stats["amp"].item()
+ mean_grad_pen += amp_stats["amp_grad_pen"].item()
+ mean_policy_pred += amp_stats["amp_policy_pred"].item()
+ mean_expert_pred += amp_stats["amp_expert_pred"].item()
+ if mean_rnd_loss is not None:
+ assert rnd_loss is not None
+ mean_rnd_loss += rnd_loss.item()
+ if mean_symmetry_loss is not None:
+ mean_symmetry_loss += symmetry_loss.item()
+
+ num_updates = self.num_learning_epochs * self.num_mini_batches
+ loss_dict = {
+ "value": mean_value_loss / num_updates,
+ "surrogate": mean_surrogate_loss / num_updates,
+ "entropy": mean_entropy / num_updates,
+ "amp": mean_amp_loss / num_updates,
+ "amp_grad_pen": mean_grad_pen / num_updates,
+ "amp_policy_pred": mean_policy_pred / num_updates,
+ "amp_expert_pred": mean_expert_pred / num_updates,
+ }
+ for name, stats in mean_stage_stats.items():
+ loss_dict[f"amp_{name}"] = stats["amp"] / num_updates
+ loss_dict[f"amp_{name}_policy_pred"] = stats["policy_pred"] / num_updates
+ loss_dict[f"amp_{name}_expert_pred"] = stats["expert_pred"] / num_updates
+ loss_dict[f"amp_{name}_policy_fallback"] = stats["fallback"] / num_updates
+ if mean_rnd_loss is not None:
+ loss_dict["rnd"] = mean_rnd_loss / num_updates
+ if mean_symmetry_loss is not None:
+ loss_dict["symmetry"] = mean_symmetry_loss / num_updates
+
+ self.storage.clear()
+ return loss_dict
+
+ def save(self) -> dict:
+ saved_dict = super().save()
+ saved_dict["discriminator_state_dict"] = self.discriminator.state_dict()
+ if self.uses_stage_amp:
+ saved_dict["stage_discriminator_state_dicts"] = {
+ name: discriminator.state_dict()
+ for name, discriminator in self.stage_discriminators.items()
+ }
+ return saved_dict
+
+ def load(self, loaded_dict: dict, load_cfg: dict | None, strict: bool) -> bool:
+ load_disc = True if load_cfg is None else load_cfg.get("discriminator", True)
+ try:
+ load_iteration = super().load(loaded_dict, load_cfg, strict)
+ except (RuntimeError, ValueError) as exc:
+ wants_optimizer = True if load_cfg is None else load_cfg.get("optimizer", True)
+ if not wants_optimizer:
+ raise
+ retry_cfg = {
+ "actor": True,
+ "critic": True,
+ "optimizer": False,
+ "iteration": True,
+ "rnd": True,
+ }
+ if load_cfg is not None:
+ retry_cfg.update(load_cfg)
+ retry_cfg["optimizer"] = False
+ print(
+ "Skipping optimizer state while loading AMP checkpoint because the "
+ f"current optimizer layout changed: {exc}"
+ )
+ load_iteration = super().load(loaded_dict, retry_cfg, strict)
+ if load_disc and self.uses_stage_amp:
+ stage_state_dicts = loaded_dict.get("stage_discriminator_state_dicts")
+ if isinstance(stage_state_dicts, dict):
+ for name, state_dict in stage_state_dicts.items():
+ if name in self.stage_discriminators:
+ self.stage_discriminators[name].load_state_dict(state_dict, strict=strict)
+ elif "discriminator_state_dict" in loaded_dict:
+ target_name = next(iter(self.stage_discriminators))
+ self.stage_discriminators[target_name].load_state_dict(
+ loaded_dict["discriminator_state_dict"],
+ strict=strict,
+ )
+ elif (
+ load_disc
+ and (not self.uses_stage_amp)
+ and "stage_discriminator_state_dicts" in loaded_dict
+ and not (load_cfg or {}).get("allow_stage_to_single_discriminator", False)
+ ):
+ print(
+ "Skipping discriminator state while loading a stage-AMP checkpoint into "
+ "a single-discriminator AMP configuration. Set "
+ "load_cfg['allow_stage_to_single_discriminator']=True to override."
+ )
+ elif load_disc and "discriminator_state_dict" in loaded_dict:
+ self.discriminator.load_state_dict(
+ loaded_dict["discriminator_state_dict"],
+ strict=strict,
+ )
+ return load_iteration
+
+ def broadcast_parameters(self) -> None:
+ model_params = [
+ self.actor.state_dict(),
+ self.critic.state_dict(),
+ (
+ {
+ name: module.state_dict()
+ for name, module in self.stage_discriminators.items()
+ }
+ if self.uses_stage_amp
+ else self.discriminator.state_dict()
+ ),
+ ]
+ if self.rnd:
+ model_params.append(self.rnd.predictor.state_dict())
+ torch.distributed.broadcast_object_list(model_params, src=0)
+ self.actor.load_state_dict(model_params[0])
+ self.critic.load_state_dict(model_params[1])
+ if self.uses_stage_amp:
+ for name, state_dict in model_params[2].items():
+ self.stage_discriminators[name].load_state_dict(state_dict)
+ else:
+ self.discriminator.load_state_dict(model_params[2])
+ if self.rnd:
+ self.rnd.predictor.load_state_dict(model_params[3])
+
+ def reduce_parameters(self) -> None:
+ all_params = chain(
+ self.actor.parameters(),
+ self.critic.parameters(),
+ self._discriminator_parameters(),
+ )
+ if self.rnd:
+ all_params = chain(all_params, self.rnd.parameters())
+ all_params = list(all_params)
+ grads = [param.grad.view(-1) for param in all_params if param.grad is not None]
+ all_grads = torch.cat(grads)
+ torch.distributed.all_reduce(all_grads, op=torch.distributed.ReduceOp.SUM)
+ all_grads /= self.gpu_world_size
+ offset = 0
+ for param in all_params:
+ if param.grad is not None:
+ numel = param.numel()
+ param.grad.data.copy_(
+ all_grads[offset : offset + numel].view_as(param.grad.data)
+ )
+ offset += numel
diff --git a/src/mjlab_playground/amp/rl/discriminator.py b/src/mjlab_playground/amp/rl/discriminator.py
new file mode 100644
index 0000000..315988c
--- /dev/null
+++ b/src/mjlab_playground/amp/rl/discriminator.py
@@ -0,0 +1,166 @@
+"""Discriminator network for adversarial motion priors."""
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from rsl_rl.modules import MLP, EmpiricalNormalization
+
+
+class AmpDiscriminator(nn.Module):
+ """Small AMP discriminator using consecutive robot-only states."""
+
+ def __init__(
+ self,
+ amp_obs_dim: int,
+ hidden_dims: tuple[int, ...] = (512, 256),
+ activation: str = "relu",
+ amp_reward_coef: float = 0.3,
+ amp_reward_scale: float = 1.0,
+ task_reward_lerp: float = 0.7,
+ grad_penalty_coef: float = 10.0,
+ normalize_input: bool = True,
+ loss_type: str = "bce",
+ reward_type: str = "softplus",
+ expert_label: float = 0.9,
+ policy_label: float = 0.1,
+ reward_clip_max: float = 1.5,
+ ) -> None:
+ super().__init__()
+ self.amp_obs_dim = int(amp_obs_dim)
+ self.input_dim = 2 * self.amp_obs_dim
+ self.amp_reward_coef = float(amp_reward_coef)
+ self.amp_reward_scale = float(amp_reward_scale)
+ self.task_reward_lerp = float(task_reward_lerp)
+ self.grad_penalty_coef = float(grad_penalty_coef)
+ self.loss_type = str(loss_type)
+ self.reward_type = str(reward_type)
+ self.expert_label = float(expert_label)
+ self.policy_label = float(policy_label)
+ self.reward_clip_max = float(reward_clip_max)
+ self.normalizer = (
+ EmpiricalNormalization(self.input_dim) if normalize_input else nn.Identity()
+ )
+ self.net = MLP(self.input_dim, 1, hidden_dims, activation)
+
+ def forward(self, state: torch.Tensor, next_state: torch.Tensor) -> torch.Tensor:
+ x = torch.cat((state, next_state), dim=-1)
+ return self.net(self.normalizer(x))
+
+ @torch.no_grad()
+ def predict_style_reward(
+ self,
+ state: torch.Tensor,
+ next_state: torch.Tensor,
+ amp_reward_scale: float | None = None,
+ ) -> tuple[torch.Tensor, torch.Tensor]:
+ """Return the AMP/style reward without mixing in task reward."""
+ training = self.training
+ self.eval()
+ disc = self.forward(state, next_state)
+ amp_reward = self.amp_reward_coef * self._style_score(disc)
+ scale = (
+ self.amp_reward_scale if amp_reward_scale is None else float(amp_reward_scale)
+ )
+ amp_reward *= scale
+ if training:
+ self.train()
+ return amp_reward.squeeze(-1), disc.squeeze(-1)
+
+ @torch.no_grad()
+ def predict_reward(
+ self,
+ state: torch.Tensor,
+ next_state: torch.Tensor,
+ task_reward: torch.Tensor,
+ amp_reward_scale: float | None = None,
+ ) -> tuple[torch.Tensor, torch.Tensor]:
+ amp_reward, disc = self.predict_style_reward(
+ state,
+ next_state,
+ amp_reward_scale=amp_reward_scale,
+ )
+ task = task_reward.view(-1, 1)
+ mixed = (1.0 - self.task_reward_lerp) * amp_reward.view(
+ -1, 1
+ ) + self.task_reward_lerp * task
+ return mixed.squeeze(-1), disc
+
+ def discriminator_loss(
+ self,
+ policy_state: torch.Tensor,
+ policy_next_state: torch.Tensor,
+ expert_state: torch.Tensor,
+ expert_next_state: torch.Tensor,
+ ) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
+ if isinstance(self.normalizer, EmpiricalNormalization):
+ with torch.no_grad():
+ self.normalizer.update(torch.cat((policy_state, policy_next_state), dim=-1))
+ self.normalizer.update(torch.cat((expert_state, expert_next_state), dim=-1))
+
+ policy_d = self.forward(policy_state, policy_next_state)
+ expert_d = self.forward(expert_state, expert_next_state)
+ if self.loss_type == "least_squares":
+ expert_loss = F.mse_loss(expert_d, torch.ones_like(expert_d))
+ policy_loss = F.mse_loss(policy_d, -torch.ones_like(policy_d))
+ elif self.loss_type == "bce":
+ expert_loss = F.binary_cross_entropy_with_logits(
+ expert_d,
+ torch.full_like(expert_d, self.expert_label),
+ )
+ policy_loss = F.binary_cross_entropy_with_logits(
+ policy_d,
+ torch.full_like(policy_d, self.policy_label),
+ )
+ else:
+ raise ValueError(f"Unsupported AMP discriminator loss_type: {self.loss_type!r}.")
+ amp_loss = 0.5 * (expert_loss + policy_loss)
+
+ grad_penalty = self._gradient_penalty(expert_state, expert_next_state)
+ total = amp_loss + grad_penalty
+ stats = {
+ "amp": amp_loss.detach(),
+ "amp_grad_pen": grad_penalty.detach(),
+ "amp_policy_pred": policy_d.mean().detach(),
+ "amp_expert_pred": expert_d.mean().detach(),
+ "amp_policy_style": self._style_score(policy_d).mean().detach(),
+ "amp_expert_style": self._style_score(expert_d).mean().detach(),
+ }
+ return total, stats
+
+ def _style_score(self, disc: torch.Tensor) -> torch.Tensor:
+ if self.reward_type == "hardware_lsgan":
+ return torch.clamp(1.0 - 0.25 * torch.square(disc - 1.0), min=0.0)
+ if self.reward_type == "legacy_lsgan":
+ expert_score = torch.clamp(0.5 * (disc + 1.0), min=0.0, max=1.0)
+ return torch.square(expert_score)
+ if self.reward_type == "sigmoid":
+ return torch.sigmoid(disc)
+ if self.reward_type == "softplus":
+ clipped = torch.clamp(disc, min=-20.0, max=20.0)
+ score = F.softplus(clipped) / F.softplus(
+ torch.ones((), device=disc.device, dtype=disc.dtype)
+ )
+ if self.reward_clip_max > 0.0:
+ score = torch.clamp(score, max=self.reward_clip_max)
+ return score
+ raise ValueError(f"Unsupported AMP reward_type: {self.reward_type!r}.")
+
+ def _gradient_penalty(
+ self,
+ expert_state: torch.Tensor,
+ expert_next_state: torch.Tensor,
+ ) -> torch.Tensor:
+ expert_data = torch.cat((expert_state, expert_next_state), dim=-1).detach()
+ expert_data.requires_grad_(True)
+ disc = self.net(self.normalizer(expert_data))
+ grad = torch.autograd.grad(
+ outputs=disc,
+ inputs=expert_data,
+ grad_outputs=torch.ones_like(disc),
+ create_graph=True,
+ retain_graph=True,
+ only_inputs=True,
+ )[0]
+ return self.grad_penalty_coef * torch.square(grad.norm(2, dim=1)).mean()
diff --git a/src/mjlab_playground/amp/rl/distributions.py b/src/mjlab_playground/amp/rl/distributions.py
new file mode 100644
index 0000000..ffedb83
--- /dev/null
+++ b/src/mjlab_playground/amp/rl/distributions.py
@@ -0,0 +1,49 @@
+"""Action distributions used by AMP/SMP robot-control tasks."""
+
+from __future__ import annotations
+
+import math
+
+import torch
+from rsl_rl.modules.distribution import GaussianDistribution
+from torch.distributions import Normal
+
+
+class ClampedGaussianDistribution(GaussianDistribution):
+ """Gaussian distribution with a bounded state-independent standard deviation."""
+
+ def __init__(
+ self,
+ output_dim: int,
+ init_std: float = 0.45,
+ std_type: str = "log",
+ min_std: float = 0.05,
+ max_std: float = 1.0,
+ ) -> None:
+ if min_std <= 0.0:
+ raise ValueError(f"min_std must be positive, got {min_std}.")
+ if max_std < min_std:
+ raise ValueError(
+ f"max_std must be greater than or equal to min_std, got {max_std} < {min_std}."
+ )
+ super().__init__(output_dim=output_dim, init_std=init_std, std_type=std_type)
+ self.min_std = float(min_std)
+ self.max_std = float(max_std)
+
+ def update(self, mlp_output: torch.Tensor) -> None:
+ """Update the Gaussian distribution from MLP output."""
+ mean = mlp_output
+ if self.std_type == "scalar":
+ std = torch.clamp(self.std_param, self.min_std, self.max_std).expand_as(mean)
+ elif self.std_type == "log":
+ log_std = torch.clamp(
+ self.log_std_param,
+ math.log(self.min_std),
+ math.log(self.max_std),
+ )
+ std = torch.exp(log_std).expand_as(mean)
+ else:
+ raise ValueError(
+ f"Unknown standard deviation type: {self.std_type}. Should be 'scalar' or 'log'."
+ )
+ self._distribution = Normal(mean, std)
diff --git a/src/mjlab_playground/amp/rl/motion_loader.py b/src/mjlab_playground/amp/rl/motion_loader.py
new file mode 100644
index 0000000..1a617b5
--- /dev/null
+++ b/src/mjlab_playground/amp/rl/motion_loader.py
@@ -0,0 +1,746 @@
+"""Retarget motion loaders for AMP-style priors."""
+
+from __future__ import annotations
+
+import os
+import pickle
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+
+from mjlab_playground.amp.amp_schema import (
+ K1_AMP_KEY_BODY_NAMES,
+ amp_obs_dim,
+)
+from mjlab_playground.asset_zoo.robots.booster_k1.k1_constants import K1_JOINT_NAMES
+
+_DEFAULT_EMPHASIS_WEIGHT = 3.0
+_DEFAULT_VELOCITY_EMPHASIS_WEIGHT = 2.0
+_PACKAGED_MOTIONS_ROOT = (
+ Path(__file__).resolve().parents[2] / "asset_zoo" / "motions" / "retargeted"
+)
+_ASSET_MOTIONS_ROOT = Path(
+ os.getenv("MJLAB_PLAYGROUND_MOTION_ROOT", str(_PACKAGED_MOTIONS_ROOT))
+).expanduser()
+
+
+def _first_existing_dir(*candidates: Path) -> Path:
+ for candidate in candidates:
+ if candidate.exists():
+ return candidate
+ return candidates[0]
+
+
+def _k1_lafan_fk_root() -> Path:
+ return _ASSET_MOTIONS_ROOT / "lafan" / "k1_lafan1_fk_pkl"
+
+
+def _t1_gmr_motion_root() -> Path:
+ return Path(
+ os.getenv(
+ "MJLAB_PLAYGROUND_T1_TRACKING_MOTION_DIR",
+ str(_ASSET_MOTIONS_ROOT / "tracking" / "booster_t1_23dof"),
+ )
+ ).expanduser()
+
+
+def _t1_lafan_fk_root() -> Path:
+ return _first_existing_dir(
+ Path(
+ os.getenv(
+ "MJLAB_PLAYGROUND_T1_LOCOMOTION_MOTION_DIR",
+ str(_ASSET_MOTIONS_ROOT / "lafan" / "booster_t1_lafan1_locomotion"),
+ )
+ ).expanduser(),
+ _t1_gmr_motion_root(),
+ )
+
+
+def default_k1_locomotion_motion_files() -> tuple[str, ...]:
+ root = _k1_lafan_fk_root()
+ patterns = ("walk*.pkl", "run*.pkl", "sprint*.pkl")
+ files = []
+ for pattern in patterns:
+ files.extend(sorted(root.glob(pattern)))
+ return tuple(str(path) for path in files)
+
+
+def default_k1_locomotion_support_motion_files() -> dict[str, str]:
+ root = _k1_lafan_fk_root()
+ return {
+ "walk": str(root / "walk1_subject5.pkl"),
+ "run": str(root / "run1_subject5.pkl"),
+ "sprint": str(root / "sprint1_subject4.pkl"),
+ }
+
+
+def default_t1_locomotion_motion_files() -> tuple[str, ...]:
+ root = _t1_lafan_fk_root()
+ patterns = (
+ "walk*.pkl",
+ "run*.pkl",
+ "sprint*.pkl",
+ "lafan1_walk*.pkl",
+ "lafan1_run*.pkl",
+ "lafan1_sprint*.pkl",
+ )
+ files = []
+ for pattern in patterns:
+ files.extend(sorted(root.glob(pattern)))
+ return tuple(str(path) for path in files)
+
+
+def default_k1_locomotion_amp_motion_groups() -> tuple[dict[str, Any], ...]:
+ return (
+ {
+ "name": "loco",
+ "files": default_k1_locomotion_motion_files(),
+ "weight": 1.0,
+ },
+ )
+
+
+def default_t1_locomotion_amp_motion_groups() -> tuple[dict[str, Any], ...]:
+ return (
+ {
+ "name": "loco",
+ "files": default_t1_locomotion_motion_files(),
+ "weight": 1.0,
+ },
+ )
+
+
+class RetargetedAmpMotionLoader:
+ """Samples consecutive humanoid AMP transitions from retarget files.
+
+ The discriminator observes a robot-only state:
+ ``joint_pos, joint_vel, root height, projected gravity, root velocity, key-body
+ positions, key-body velocities``. This follows a common humanoid AMP
+ convention and gives the prior visibility into end-effector and support-foot
+ geometry. Key body positions come from GMR-exported
+ ``local_body_pos`` fields, not an ad hoc loader-side FK pass.
+ """
+
+ def __init__(
+ self,
+ motion_files: tuple[str, ...] | list[str] = (),
+ device: str | torch.device = "cpu",
+ expected_dof: int = 22,
+ motion_groups: Any | None = None,
+ target_fps: float | None = None,
+ joint_names: tuple[str, ...] | None = K1_JOINT_NAMES,
+ key_body_names: tuple[str, ...] | None = K1_AMP_KEY_BODY_NAMES,
+ emphasis_frame_offset_range: tuple[int, int] | list[int] | None = None,
+ emphasis_weight: float | None = None,
+ velocity_emphasis_weight: float | None = None,
+ ) -> None:
+ self.device = torch.device(device)
+ self.expected_dof = int(expected_dof)
+ self.joint_names = tuple(joint_names or K1_JOINT_NAMES)
+ self.target_fps = None if target_fps is None else max(float(target_fps), 1.0e-6)
+ if len(self.joint_names) != self.expected_dof:
+ raise ValueError(
+ "AMP joint order has "
+ f"{len(self.joint_names)} names, expected {self.expected_dof}."
+ )
+ self.key_body_names = tuple(key_body_names or K1_AMP_KEY_BODY_NAMES)
+ self.expected_amp_obs_dim = amp_obs_dim(
+ self.expected_dof,
+ len(self.key_body_names),
+ )
+
+ groups = self._normalize_groups(
+ motion_files=motion_files,
+ motion_groups=motion_groups,
+ emphasis_frame_offset_range=emphasis_frame_offset_range,
+ emphasis_weight=emphasis_weight,
+ velocity_emphasis_weight=velocity_emphasis_weight,
+ )
+ if not groups:
+ raise ValueError("No AMP motion files were provided.")
+
+ loaded_groups = []
+ for group in groups:
+ states = []
+ next_states = []
+ transition_weights = []
+ for motion_file in group["files"]:
+ file_states, file_weights = self._load_file(
+ Path(motion_file),
+ emphasis_frame_offset_range=group.get("emphasis_frame_offset_range"),
+ emphasis_weight=group.get("emphasis_weight", 0.0),
+ velocity_emphasis_weight=group.get("velocity_emphasis_weight", 0.0),
+ )
+ if file_states.shape[0] < 2:
+ continue
+ states.append(file_states[:-1])
+ next_states.append(file_states[1:])
+ transition_weights.append(file_weights)
+ if not states:
+ continue
+ sample_weights = np.concatenate(transition_weights, axis=0).astype(np.float32)
+ if not np.isfinite(sample_weights).all() or np.sum(sample_weights) <= 0.0:
+ sample_weights = np.ones_like(sample_weights, dtype=np.float32)
+ loaded_groups.append(
+ {
+ "name": group["name"],
+ "weight": float(group["weight"]),
+ "state": torch.tensor(
+ np.concatenate(states, axis=0),
+ dtype=torch.float32,
+ device=self.device,
+ ),
+ "next_state": torch.tensor(
+ np.concatenate(next_states, axis=0),
+ dtype=torch.float32,
+ device=self.device,
+ ),
+ "sample_weights": torch.tensor(
+ sample_weights,
+ dtype=torch.float32,
+ device=self.device,
+ ),
+ }
+ )
+
+ if not loaded_groups:
+ raise ValueError("No AMP transitions were loaded from the provided motion files.")
+
+ self._groups = loaded_groups
+ self.group_names = tuple(group["name"] for group in loaded_groups)
+ self.group_counts = {
+ group["name"]: int(group["state"].shape[0]) for group in loaded_groups
+ }
+ self.group_sample_weight_sums = {
+ group["name"]: float(group["sample_weights"].sum().item())
+ for group in loaded_groups
+ }
+ self.group_weights = torch.tensor(
+ [max(float(group["weight"]), 0.0) for group in loaded_groups],
+ dtype=torch.float32,
+ device=self.device,
+ )
+ if torch.sum(self.group_weights) <= 0.0:
+ self.group_weights[:] = 1.0
+ self.group_weights = self.group_weights / torch.sum(self.group_weights)
+ self.state = torch.cat([group["state"] for group in loaded_groups], dim=0)
+ self.next_state = torch.cat([group["next_state"] for group in loaded_groups], dim=0)
+ self.sample_weights = torch.cat(
+ [group["sample_weights"] for group in loaded_groups], dim=0
+ )
+ self.amp_obs_dim = int(self.state.shape[1])
+
+ @property
+ def num_transitions(self) -> int:
+ return int(self.state.shape[0])
+
+ def sample(self, batch_size: int) -> tuple[torch.Tensor, torch.Tensor]:
+ if len(self._groups) == 1:
+ group = self._groups[0]
+ idx = torch.multinomial(
+ group["sample_weights"],
+ batch_size,
+ replacement=True,
+ )
+ return group["state"][idx], group["next_state"][idx]
+
+ group_ids = torch.multinomial(self.group_weights, batch_size, replacement=True)
+ state = torch.empty(batch_size, self.amp_obs_dim, device=self.device)
+ next_state = torch.empty_like(state)
+ for group_id, group in enumerate(self._groups):
+ mask = group_ids == group_id
+ num = int(mask.sum().item())
+ if num == 0:
+ continue
+ idx = torch.multinomial(group["sample_weights"], num, replacement=True)
+ state[mask] = group["state"][idx]
+ next_state[mask] = group["next_state"][idx]
+ return state, next_state
+
+ def _normalize_groups(
+ self,
+ *,
+ motion_files: tuple[str, ...] | list[str],
+ motion_groups: Any | None,
+ emphasis_frame_offset_range: tuple[int, int] | list[int] | None,
+ emphasis_weight: float | None,
+ velocity_emphasis_weight: float | None,
+ ) -> list[dict[str, Any]]:
+ if motion_groups is None:
+ files = tuple(str(path) for path in motion_files)
+ default_window = _normalize_frame_offset_range(
+ emphasis_frame_offset_range,
+ field_name="emphasis_frame_offset_range",
+ )
+ default_emphasis_weight = (
+ _DEFAULT_EMPHASIS_WEIGHT
+ if emphasis_weight is None and default_window is not None
+ else float(emphasis_weight or 0.0)
+ )
+ default_velocity_weight = (
+ _DEFAULT_VELOCITY_EMPHASIS_WEIGHT
+ if velocity_emphasis_weight is None and default_window is not None
+ else float(velocity_emphasis_weight or 0.0)
+ )
+ return (
+ [
+ {
+ "name": "default",
+ "files": files,
+ "weight": 1.0,
+ "emphasis_frame_offset_range": default_window,
+ "emphasis_weight": default_emphasis_weight,
+ "velocity_emphasis_weight": default_velocity_weight,
+ }
+ ]
+ if files
+ else []
+ )
+
+ if isinstance(motion_groups, dict):
+ iterable = [
+ {"name": name, **value}
+ if isinstance(value, dict)
+ else {"name": name, "files": value}
+ for name, value in motion_groups.items()
+ ]
+ else:
+ iterable = list(motion_groups)
+
+ groups = []
+ for i, group in enumerate(iterable):
+ if not isinstance(group, dict):
+ raise TypeError(f"AMP motion group {i} must be a dict, got {type(group)!r}.")
+ files = tuple(str(path) for path in group.get("files", ()))
+ if not files:
+ continue
+ emphasis_window = _normalize_frame_offset_range(
+ group.get("emphasis_frame_offset_range"),
+ field_name="emphasis_frame_offset_range",
+ )
+ emphasis_weight = float(
+ group.get(
+ "emphasis_weight",
+ _DEFAULT_EMPHASIS_WEIGHT if emphasis_window is not None else 0.0,
+ )
+ )
+ velocity_weight = float(
+ group.get(
+ "velocity_emphasis_weight",
+ _DEFAULT_VELOCITY_EMPHASIS_WEIGHT if emphasis_window is not None else 0.0,
+ )
+ )
+ groups.append(
+ {
+ "name": str(group.get("name", f"group_{i}")),
+ "files": files,
+ "weight": float(group.get("weight", 1.0)),
+ "emphasis_frame_offset_range": emphasis_window,
+ "emphasis_weight": emphasis_weight,
+ "velocity_emphasis_weight": velocity_weight,
+ }
+ )
+ return groups
+
+ def _load_file(
+ self,
+ path: Path,
+ emphasis_frame_offset_range: tuple[int, int] | None = None,
+ emphasis_weight: float = 0.0,
+ velocity_emphasis_weight: float = 0.0,
+ ) -> tuple[np.ndarray, np.ndarray]:
+ if not path.exists():
+ raise FileNotFoundError(f"AMP motion file not found: {path}")
+ if path.suffix.lower() == ".csv":
+ return self._load_gmr_fk_pkl_for_csv(
+ path,
+ emphasis_frame_offset_range=emphasis_frame_offset_range,
+ emphasis_weight=emphasis_weight,
+ velocity_emphasis_weight=velocity_emphasis_weight,
+ )
+ if path.suffix.lower() not in {".pkl", ".pickle"}:
+ raise ValueError(f"Unsupported AMP motion file type: {path}")
+ with path.open("rb") as f:
+ data = pickle.load(f)
+ joint_pos = np.asarray(data["dof_pos"], dtype=np.float32)
+ fps = float(data.get("fps", 30.0))
+ root_pos = np.asarray(data["root_pos"], dtype=np.float32)
+ root_quat_wxyz = _xyzw_to_wxyz(np.asarray(data["root_rot"], dtype=np.float32))
+ body_names, body_pos = self._body_positions_from_pickle(data, path=path)
+ root_pos, root_quat_wxyz, body_pos, joint_pos, fps = _resample_state_parts(
+ root_pos,
+ root_quat_wxyz,
+ body_pos,
+ joint_pos,
+ src_fps=fps,
+ target_fps=self.target_fps,
+ )
+ key_body_pos = self._key_body_pos_from_body_positions(
+ body_names,
+ body_pos,
+ joint_pos=joint_pos,
+ path=path,
+ )
+ emphasis_frame = None
+ if emphasis_frame_offset_range is not None or velocity_emphasis_weight > 0.0:
+ emphasis_frame = self._infer_emphasis_frame(
+ body_names,
+ body_pos,
+ root_quat_wxyz=root_quat_wxyz,
+ path=path,
+ )
+ transition_weights = self._transition_sample_weights(
+ body_names,
+ body_pos,
+ num_frames=joint_pos.shape[0],
+ fps=fps,
+ emphasis_frame=emphasis_frame,
+ emphasis_frame_offset_range=emphasis_frame_offset_range,
+ emphasis_weight=emphasis_weight,
+ velocity_emphasis_weight=velocity_emphasis_weight,
+ path=path,
+ )
+ return (
+ self._amp_state_obs(
+ joint_pos,
+ root_pos=root_pos,
+ root_quat_wxyz=root_quat_wxyz,
+ key_body_pos_b=key_body_pos,
+ fps=fps,
+ path=path,
+ ),
+ transition_weights,
+ )
+
+ def _load_gmr_fk_pkl_for_csv(
+ self,
+ path: Path,
+ emphasis_frame_offset_range: tuple[int, int] | None = None,
+ emphasis_weight: float = 0.0,
+ velocity_emphasis_weight: float = 0.0,
+ ) -> tuple[np.ndarray, np.ndarray]:
+ candidates = (
+ path.with_suffix(".pkl"),
+ path.parent.parent / f"{path.parent.name}_fk_pkl" / f"{path.stem}.pkl",
+ path.parent.parent / "k1_lafan1_fk_pkl" / f"{path.stem}.pkl",
+ )
+ for candidate in candidates:
+ if candidate.exists():
+ return self._load_file(
+ candidate,
+ emphasis_frame_offset_range=emphasis_frame_offset_range,
+ emphasis_weight=emphasis_weight,
+ velocity_emphasis_weight=velocity_emphasis_weight,
+ )
+ raise ValueError(
+ f"{path} is a root/joint CSV without GMR key-body data. AMP key-body "
+ "observations require a converted pkl with local_body_pos/link_body_list. "
+ f"Checked: {', '.join(str(candidate) for candidate in candidates)}."
+ )
+
+ def _amp_state_obs(
+ self,
+ joint_pos: np.ndarray,
+ *,
+ root_pos: np.ndarray,
+ root_quat_wxyz: np.ndarray,
+ key_body_pos_b: np.ndarray,
+ fps: float,
+ path: Path,
+ ) -> np.ndarray:
+ if joint_pos.ndim != 2 or joint_pos.shape[1] != self.expected_dof:
+ raise ValueError(
+ f"{path} has dof_pos shape {joint_pos.shape}, expected [T, {self.expected_dof}]."
+ )
+ if root_pos.shape != (joint_pos.shape[0], 3):
+ raise ValueError(f"{path} has root_pos shape {root_pos.shape}, expected [T, 3].")
+ if root_quat_wxyz.shape != (joint_pos.shape[0], 4):
+ raise ValueError(
+ f"{path} has root_rot shape {root_quat_wxyz.shape}, expected [T, 4]."
+ )
+ if key_body_pos_b.shape != (joint_pos.shape[0], len(self.key_body_names), 3):
+ raise ValueError(
+ f"{path} has key-body position shape {key_body_pos_b.shape}, "
+ f"expected [T, {len(self.key_body_names)}, 3]."
+ )
+ dt = 1.0 / max(fps, 1.0e-6)
+ joint_vel = np.gradient(joint_pos, dt, axis=0).astype(np.float32)
+ root_quat_wxyz = _normalize_quat_wxyz(root_quat_wxyz)
+ root_lin_vel_w = np.gradient(root_pos, dt, axis=0).astype(np.float32)
+ root_lin_vel_b = _quat_apply_inverse_wxyz(root_quat_wxyz, root_lin_vel_w)
+ root_ang_vel_b = _quat_to_body_ang_vel_wxyz(root_quat_wxyz, dt)
+ projected_gravity_b = _quat_apply_inverse_wxyz(
+ root_quat_wxyz,
+ np.tile(np.array([[0.0, 0.0, -1.0]], dtype=np.float32), (joint_pos.shape[0], 1)),
+ )
+ key_body_vel_b = np.gradient(key_body_pos_b, dt, axis=0).astype(np.float32)
+ obs = np.concatenate(
+ (
+ joint_pos.astype(np.float32),
+ joint_vel,
+ root_pos[:, 2:3].astype(np.float32),
+ projected_gravity_b.astype(np.float32),
+ root_lin_vel_b.astype(np.float32),
+ root_ang_vel_b.astype(np.float32),
+ key_body_pos_b.reshape(joint_pos.shape[0], -1).astype(np.float32),
+ key_body_vel_b.reshape(joint_pos.shape[0], -1).astype(np.float32),
+ ),
+ axis=1,
+ )
+ if obs.shape[1] != self.expected_amp_obs_dim:
+ raise ValueError(
+ f"{path} produced AMP obs dim {obs.shape[1]}, "
+ f"expected {self.expected_amp_obs_dim}."
+ )
+ return np.nan_to_num(obs, nan=0.0, posinf=0.0, neginf=0.0)
+
+ def _body_positions_from_pickle(
+ self,
+ data: dict[str, Any],
+ *,
+ path: Path,
+ ) -> tuple[list[str], np.ndarray]:
+ if data.get("local_body_pos") is None or "link_body_list" not in data:
+ raise ValueError(
+ f"{path} does not contain GMR local_body_pos/link_body_list. "
+ "Regenerate this motion with GMR KinematicsModel.forward_kinematics before AMP training."
+ )
+ body_names = list(data["link_body_list"])
+ body_pos = np.asarray(data["local_body_pos"], dtype=np.float32)
+ if body_pos.ndim != 3 or body_pos.shape[2] != 3:
+ raise ValueError(
+ f"{path} has local_body_pos shape {body_pos.shape}, expected [T, B, 3]."
+ )
+ return body_names, body_pos
+
+ def _key_body_pos_from_body_positions(
+ self,
+ body_names: list[str],
+ body_pos: np.ndarray,
+ *,
+ joint_pos: np.ndarray,
+ path: Path,
+ ) -> np.ndarray:
+ ids = []
+ for name in self.key_body_names:
+ try:
+ ids.append(body_names.index(name))
+ except ValueError as exc:
+ raise ValueError(f"{path} does not contain key body {name!r}.") from exc
+ key_body_pos = body_pos[:, ids, :]
+ if key_body_pos.shape[0] != joint_pos.shape[0]:
+ raise ValueError(
+ f"{path} has local_body_pos length {key_body_pos.shape[0]}, "
+ f"expected {joint_pos.shape[0]}."
+ )
+ return key_body_pos
+
+ def _infer_emphasis_frame(
+ self,
+ body_names: list[str],
+ body_pos: np.ndarray,
+ *,
+ root_quat_wxyz: np.ndarray | None = None,
+ path: Path,
+ ) -> int | None:
+ try:
+ right_foot_id = body_names.index("right_foot_link")
+ except ValueError as exc:
+ raise ValueError(
+ f"{path} does not contain right_foot_link in link_body_list."
+ ) from exc
+ right_foot_pos = body_pos[:, right_foot_id, :]
+ if root_quat_wxyz is not None:
+ root_roll_pitch = _roll_pitch_quat_wxyz(root_quat_wxyz)
+ right_foot_pos = _quat_apply_wxyz(root_roll_pitch, right_foot_pos)
+ return int(np.argmax(right_foot_pos[:, 0]))
+
+ def _transition_sample_weights(
+ self,
+ body_names: list[str],
+ body_pos: np.ndarray,
+ *,
+ num_frames: int,
+ fps: float,
+ emphasis_frame: int | None,
+ emphasis_frame_offset_range: tuple[int, int] | None,
+ emphasis_weight: float,
+ velocity_emphasis_weight: float,
+ path: Path,
+ ) -> np.ndarray:
+ transition_weights = np.ones(max(num_frames - 1, 0), dtype=np.float32)
+ if transition_weights.size == 0:
+ return transition_weights
+ if emphasis_frame_offset_range is None and velocity_emphasis_weight <= 0.0:
+ return transition_weights
+ try:
+ right_foot_id = body_names.index("right_foot_link")
+ except ValueError as exc:
+ raise ValueError(
+ f"{path} does not contain right_foot_link in link_body_list."
+ ) from exc
+ if emphasis_frame is None:
+ emphasis_frame = int(np.argmax(body_pos[:, right_foot_id, 0]))
+ if emphasis_frame_offset_range is not None and emphasis_weight > 0.0:
+ offset_min, offset_max = emphasis_frame_offset_range
+ start = max(0, emphasis_frame + offset_min)
+ stop = min(num_frames - 1, emphasis_frame + offset_max)
+ transition_ids = np.arange(num_frames - 1, dtype=np.int64)
+ in_window = (transition_ids >= start) & (transition_ids <= stop)
+ transition_weights[in_window] += float(emphasis_weight)
+
+ if velocity_emphasis_weight > 0.0:
+ dt = 1.0 / max(float(fps), 1.0e-6)
+ right_foot_vel = np.gradient(body_pos[:, right_foot_id, :], dt, axis=0)
+ right_foot_speed = np.linalg.norm(right_foot_vel, axis=1)
+ transition_speed = 0.5 * (right_foot_speed[:-1] + right_foot_speed[1:])
+ speed_scale = float(np.percentile(transition_speed, 90.0))
+ if speed_scale > 1.0e-6:
+ transition_weights += float(velocity_emphasis_weight) * np.clip(
+ transition_speed / speed_scale,
+ 0.0,
+ 1.0,
+ ).astype(np.float32)
+ return np.clip(transition_weights, 1.0e-6, None)
+
+
+def _xyzw_to_wxyz(quat_xyzw: np.ndarray) -> np.ndarray:
+ if quat_xyzw.ndim != 2 or quat_xyzw.shape[1] != 4:
+ raise ValueError(f"Expected quaternion shape [T, 4], got {quat_xyzw.shape}.")
+ return _normalize_quat_wxyz(quat_xyzw[:, [3, 0, 1, 2]])
+
+
+def _normalize_frame_offset_range(
+ value: tuple[int, int] | list[int] | None,
+ *,
+ field_name: str,
+) -> tuple[int, int] | None:
+ if value is None:
+ return None
+ if len(value) != 2:
+ raise ValueError(f"{field_name} must have two values, got {value!r}.")
+ start, stop = int(value[0]), int(value[1])
+ if start > stop:
+ raise ValueError(f"{field_name} start must be <= stop, got {(start, stop)!r}.")
+ return start, stop
+
+
+def _resample_array(
+ array: np.ndarray,
+ *,
+ src_fps: float,
+ target_fps: float | None,
+) -> np.ndarray:
+ array = np.asarray(array, dtype=np.float32)
+ if target_fps is None or np.isclose(float(target_fps), float(src_fps)):
+ return array
+ if array.shape[0] <= 1:
+ return array
+ src_fps = max(float(src_fps), 1.0e-6)
+ target_fps = max(float(target_fps), 1.0e-6)
+ duration = (array.shape[0] - 1) / src_fps
+ target_frames = max(int(round(duration * target_fps)) + 1, 2)
+ src_t = np.arange(array.shape[0], dtype=np.float32) / src_fps
+ target_t = np.linspace(0.0, duration, target_frames, dtype=np.float32)
+ flat = array.reshape(array.shape[0], -1)
+ resampled = np.empty((target_frames, flat.shape[1]), dtype=np.float32)
+ for dim in range(flat.shape[1]):
+ resampled[:, dim] = np.interp(target_t, src_t, flat[:, dim]).astype(np.float32)
+ return resampled.reshape((target_frames, *array.shape[1:]))
+
+
+def _resample_state_parts(
+ root_pos: np.ndarray,
+ root_quat_wxyz: np.ndarray,
+ body_pos: np.ndarray,
+ joint_pos: np.ndarray,
+ *,
+ src_fps: float,
+ target_fps: float | None,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, float]:
+ fps = float(target_fps) if target_fps is not None else float(src_fps)
+ root_pos = _resample_array(root_pos, src_fps=src_fps, target_fps=target_fps)
+ root_quat = _resample_array(root_quat_wxyz, src_fps=src_fps, target_fps=target_fps)
+ root_quat = _normalize_quat_wxyz(root_quat)
+ body_pos = _resample_array(body_pos, src_fps=src_fps, target_fps=target_fps)
+ joint_pos = _resample_array(joint_pos, src_fps=src_fps, target_fps=target_fps)
+ return root_pos, root_quat, body_pos, joint_pos, fps
+
+
+def _normalize_quat_wxyz(quat: np.ndarray) -> np.ndarray:
+ quat = np.asarray(quat, dtype=np.float32)
+ norm = np.linalg.norm(quat, axis=-1, keepdims=True)
+ return quat / np.clip(norm, 1.0e-6, None)
+
+
+def _quat_apply_inverse_wxyz(quat: np.ndarray, vec: np.ndarray) -> np.ndarray:
+ quat = _normalize_quat_wxyz(quat)
+ vec = np.asarray(vec, dtype=np.float32)
+ if quat.ndim == vec.ndim - 1:
+ quat = quat[:, None, :]
+ u = quat[..., 1:]
+ s = quat[..., :1]
+ return vec - 2.0 * s * np.cross(u, vec) + 2.0 * np.cross(u, np.cross(u, vec))
+
+
+def _quat_apply_wxyz(quat: np.ndarray, vec: np.ndarray) -> np.ndarray:
+ quat = _normalize_quat_wxyz(quat)
+ vec = np.asarray(vec, dtype=np.float32)
+ if quat.ndim == vec.ndim - 1:
+ quat = quat[:, None, :]
+ u = quat[..., 1:]
+ s = quat[..., :1]
+ return (
+ 2.0 * np.sum(u * vec, axis=-1, keepdims=True) * u
+ + (s * s - np.sum(u * u, axis=-1, keepdims=True)) * vec
+ + 2.0 * s * np.cross(u, vec)
+ ).astype(np.float32)
+
+
+def _roll_pitch_quat_wxyz(quat: np.ndarray) -> np.ndarray:
+ quat = _normalize_quat_wxyz(quat)
+ w, x, y, z = np.moveaxis(quat, -1, 0)
+ roll = np.arctan2(2.0 * (w * x + y * z), 1.0 - 2.0 * (x * x + y * y))
+ pitch = np.arcsin(np.clip(2.0 * (w * y - z * x), -1.0, 1.0))
+ half_roll = 0.5 * roll
+ half_pitch = 0.5 * pitch
+ cr = np.cos(half_roll)
+ sr = np.sin(half_roll)
+ cp = np.cos(half_pitch)
+ sp = np.sin(half_pitch)
+ return _normalize_quat_wxyz(
+ np.stack(
+ (
+ cr * cp,
+ sr * cp,
+ cr * sp,
+ -sr * sp,
+ ),
+ axis=-1,
+ ).astype(np.float32)
+ )
+
+
+def _quat_mul_wxyz(a: np.ndarray, b: np.ndarray) -> np.ndarray:
+ aw, ax, ay, az = np.moveaxis(a, -1, 0)
+ bw, bx, by, bz = np.moveaxis(b, -1, 0)
+ return np.stack(
+ (
+ aw * bw - ax * bx - ay * by - az * bz,
+ aw * bx + ax * bw + ay * bz - az * by,
+ aw * by - ax * bz + ay * bw + az * bx,
+ aw * bz + ax * by - ay * bx + az * bw,
+ ),
+ axis=-1,
+ ).astype(np.float32)
+
+
+def _quat_to_body_ang_vel_wxyz(quat: np.ndarray, dt: float) -> np.ndarray:
+ quat = _normalize_quat_wxyz(quat)
+ qdot = np.gradient(quat, dt, axis=0).astype(np.float32)
+ quat_inv = quat.copy()
+ quat_inv[:, 1:] *= -1.0
+ omega_quat = _quat_mul_wxyz(quat_inv, qdot)
+ return (2.0 * omega_quat[:, 1:]).astype(np.float32)
diff --git a/src/mjlab_playground/amp/rl/replay_buffer.py b/src/mjlab_playground/amp/rl/replay_buffer.py
new file mode 100644
index 0000000..839c5ab
--- /dev/null
+++ b/src/mjlab_playground/amp/rl/replay_buffer.py
@@ -0,0 +1,57 @@
+"""Simple transition replay buffers for AMP state pairs."""
+
+from __future__ import annotations
+
+import torch
+
+
+class AmpTransitionBuffer:
+ """Circular replay buffer storing AMP state transitions."""
+
+ def __init__(
+ self,
+ capacity: int,
+ amp_obs_dim: int,
+ device: str | torch.device,
+ ) -> None:
+ self.capacity = int(capacity)
+ self.amp_obs_dim = int(amp_obs_dim)
+ self.device = torch.device(device)
+ self.state = torch.zeros(self.capacity, self.amp_obs_dim, device=self.device)
+ self.next_state = torch.zeros_like(self.state)
+ self._pos = 0
+ self._size = 0
+
+ @property
+ def size(self) -> int:
+ return self._size
+
+ def insert(self, state: torch.Tensor, next_state: torch.Tensor) -> None:
+ state = state.detach().to(self.device)
+ next_state = next_state.detach().to(self.device)
+ num = state.shape[0]
+ if num >= self.capacity:
+ self.state[:] = state[-self.capacity :]
+ self.next_state[:] = next_state[-self.capacity :]
+ self._pos = 0
+ self._size = self.capacity
+ return
+
+ end = self._pos + num
+ if end <= self.capacity:
+ self.state[self._pos : end] = state
+ self.next_state[self._pos : end] = next_state
+ else:
+ first = self.capacity - self._pos
+ self.state[self._pos :] = state[:first]
+ self.next_state[self._pos :] = next_state[:first]
+ self.state[: end % self.capacity] = state[first:]
+ self.next_state[: end % self.capacity] = next_state[first:]
+ self._pos = end % self.capacity
+ self._size = min(self.capacity, self._size + num)
+
+ def sample(self, batch_size: int) -> tuple[torch.Tensor, torch.Tensor]:
+ if self._size <= 0:
+ raise RuntimeError("Cannot sample from an empty AMP replay buffer.")
+ idx = torch.randint(self._size, (batch_size,), device=self.device)
+ return self.state[idx], self.next_state[idx]
diff --git a/src/mjlab_playground/amp/rl/runner.py b/src/mjlab_playground/amp/rl/runner.py
new file mode 100644
index 0000000..386300c
--- /dev/null
+++ b/src/mjlab_playground/amp/rl/runner.py
@@ -0,0 +1,379 @@
+"""On-policy runner with adversarial motion-prior rewards."""
+
+# pyright: reportArgumentType=false, reportAttributeAccessIssue=false
+# pyright: reportCallIssue=false, reportOptionalMemberAccess=false
+
+from __future__ import annotations
+
+import os
+import time
+
+import torch
+from mjlab.rl import MjlabOnPolicyRunner
+from rsl_rl.utils import check_nan
+
+from mjlab_playground.amp.mdp.observations import amp_robot_obs
+
+
+class AmpOnPolicyRunner(MjlabOnPolicyRunner):
+ """Runner that computes AMP observations around env.step()."""
+
+ _VERBOSE_LOG_VALUES = {"1", "true", "yes", "on"}
+
+ def _amp_obs(self) -> torch.Tensor:
+ return amp_robot_obs(self.env.unwrapped).to(self.device)
+
+ def _amp_cfg(self) -> dict:
+ return dict(
+ getattr(self.alg, "amp_cfg", {}) or self.cfg["algorithm"].get("amp_cfg", {})
+ )
+
+ def _log_namespace(self) -> str:
+ return str(self._amp_cfg().get("log_namespace", "amp"))
+
+ def _verbose_log_enabled(self) -> bool:
+ return (
+ os.environ.get("MJLAB_AMP_VERBOSE_LOG", "").lower() in self._VERBOSE_LOG_VALUES
+ )
+
+ def _stage_weights(self, valid: torch.Tensor) -> dict[str, torch.Tensor] | None:
+ if not getattr(self.alg, "uses_stage_amp", False):
+ return None
+ env = self.env.unwrapped
+ stage_weight_map = getattr(env, "_amp_stage_weight_map", None)
+ if isinstance(stage_weight_map, dict) and stage_weight_map:
+ resolved: dict[str, torch.Tensor] = {}
+ for name, weight in stage_weight_map.items():
+ resolved[name] = weight.to(self.device).view(-1)[valid.view(-1)]
+ return resolved
+
+ raise RuntimeError(
+ "Stage AMP is enabled but env._amp_stage_weight_map is not available."
+ )
+
+ def _task_reward_rate(self, task_rewards: torch.Tensor) -> tuple[torch.Tensor, float]:
+ env = self.env.unwrapped
+ scale = 1.0
+ if getattr(env.cfg, "scale_rewards_by_dt", True):
+ scale = max(float(getattr(env, "step_dt", 1.0)), 1.0e-6)
+ return task_rewards / scale, scale
+
+ def _style_reward_ceiling(
+ self,
+ task_rewards: torch.Tensor,
+ stage_weights: dict[str, torch.Tensor] | None,
+ ) -> torch.Tensor:
+ def discriminator_ceiling(discriminator) -> float:
+ return float(discriminator.amp_reward_coef) * float(
+ discriminator.amp_reward_scale
+ )
+
+ if not getattr(self.alg, "uses_stage_amp", False):
+ discriminator = getattr(self.alg, "discriminator", None)
+ ceiling = (
+ discriminator_ceiling(discriminator) if discriminator is not None else 0.0
+ )
+ return torch.full_like(task_rewards, ceiling)
+
+ total_weight = torch.zeros_like(task_rewards)
+ ceiling = torch.zeros_like(task_rewards)
+ for name, discriminator in self.alg.stage_discriminators.items():
+ if stage_weights is None:
+ weight = torch.full_like(task_rewards, 1.0 / len(self.alg.stage_discriminators))
+ else:
+ weight = stage_weights.get(name, torch.zeros_like(task_rewards))
+ weight = weight.to(device=task_rewards.device, dtype=task_rewards.dtype).view(
+ -1
+ )
+ ceiling += weight * discriminator_ceiling(discriminator)
+ total_weight += weight
+ return ceiling / total_weight.clamp_min(1.0e-6)
+
+ def _style_reward_gate(
+ self,
+ style_rewards: torch.Tensor,
+ dones: torch.Tensor,
+ ) -> tuple[torch.Tensor, torch.Tensor | None]:
+ gate_cfg = self._amp_cfg().get("style_reward_gate")
+ if not gate_cfg:
+ return style_rewards, None
+
+ env = self.env.unwrapped
+ entity_name = str(gate_cfg.get("entity_name", "robot"))
+ asset = env.scene[entity_name]
+ healthy = torch.ones_like(style_rewards, dtype=torch.bool, device=self.device)
+
+ if gate_cfg.get("zero_on_done", True):
+ healthy = healthy & (~dones.to(self.device).bool().view(-1))
+ if gate_cfg.get("min_root_height") is not None:
+ root_height = asset.data.root_link_pos_w[:, 2].to(self.device).view(-1)
+ healthy = healthy & (root_height >= float(gate_cfg["min_root_height"]))
+ if gate_cfg.get("min_up_z") is not None:
+ up_z = (-asset.data.projected_gravity_b[:, 2]).to(self.device).view(-1)
+ healthy = healthy & (up_z >= float(gate_cfg["min_up_z"]))
+
+ gated_style = torch.where(healthy, style_rewards, torch.zeros_like(style_rewards))
+ return gated_style, healthy.float()
+
+ def _apply_amp_reward_schedule(self) -> tuple[float, float]:
+ amp_cfg = dict(getattr(self.alg, "amp_cfg", {}))
+ if not amp_cfg:
+ amp_cfg = self.cfg["algorithm"].get("amp_cfg", {})
+ schedule = amp_cfg.get("reward_mix_schedule", ())
+
+ task_reward_lerp = float(getattr(self.alg, "task_reward_lerp", 0.0))
+ main_discriminator = getattr(self.alg, "discriminator", None)
+ reward_coef = (
+ float(getattr(main_discriminator, "amp_reward_coef", 0.0))
+ if main_discriminator is not None
+ else 0.0
+ )
+
+ for stage in schedule:
+ if self.env.unwrapped.common_step_counter < int(stage["step"]):
+ continue
+ if "task_reward_lerp" in stage:
+ task_reward_lerp = float(stage["task_reward_lerp"])
+ if "reward_coef" in stage:
+ reward_coef = float(stage["reward_coef"])
+
+ self.alg.task_reward_lerp = task_reward_lerp
+ discriminators = []
+ if main_discriminator is not None:
+ discriminators.append(main_discriminator)
+ discriminators.extend(getattr(self.alg, "stage_discriminators", {}).values())
+
+ seen: set[int] = set()
+ for discriminator in discriminators:
+ disc_id = id(discriminator)
+ if disc_id in seen:
+ continue
+ seen.add(disc_id)
+ discriminator.task_reward_lerp = task_reward_lerp
+ discriminator.amp_reward_coef = reward_coef
+
+ return task_reward_lerp, reward_coef
+
+ def _disable_auto_reset_for_terminal_amp(self) -> None:
+ """Let the runner capture terminal AMP transitions before resetting envs."""
+ self.env.unwrapped.cfg.auto_reset = False
+
+ def _reset_done_envs(self, dones: torch.Tensor) -> torch.Tensor | None:
+ done_ids = dones.to(self.env.device).bool().nonzero(as_tuple=False).squeeze(-1)
+ if done_ids.numel() == 0:
+ return None
+ self.env.unwrapped.reset(env_ids=done_ids)
+ return self.env.get_observations().to(self.device)
+
+ def _learning_start_iteration(self) -> int:
+ start_it = self.current_learning_iteration
+ if getattr(self, "_resume_next_iteration", False):
+ start_it += 1
+ self._resume_next_iteration = False
+ return start_it
+
+ def learn(
+ self, num_learning_iterations: int, init_at_random_ep_len: bool = False
+ ) -> None:
+ if self._amp_cfg().get("disable_init_at_random_ep_len", False):
+ init_at_random_ep_len = False
+ if init_at_random_ep_len:
+ self.env.episode_length_buf = torch.randint_like(
+ self.env.episode_length_buf,
+ high=int(self.env.max_episode_length),
+ )
+
+ obs = self.env.get_observations().to(self.device)
+ self.alg.train_mode()
+
+ if self.is_distributed:
+ print(f"Synchronizing parameters for rank {self.gpu_global_rank}...")
+ self.alg.broadcast_parameters()
+
+ self._disable_auto_reset_for_terminal_amp()
+ self.logger.init_logging_writer()
+ verbose_log = self._verbose_log_enabled()
+ log_namespace = self._log_namespace()
+
+ start_it = self._learning_start_iteration()
+ total_it = start_it + num_learning_iterations
+ for it in range(start_it, total_it):
+ start = time.time()
+ with torch.inference_mode():
+ for _ in range(self.cfg["num_steps_per_env"]):
+ amp_obs = self._amp_obs()
+ actions = self.alg.act(obs)
+ obs, task_rewards, dones, extras = self.env.step(actions.to(self.env.device))
+ if self.cfg.get("check_for_nan", True):
+ check_nan(obs, task_rewards, dones)
+ next_amp_obs = self._amp_obs()
+
+ obs = obs.to(self.device)
+ task_rewards = task_rewards.to(self.device)
+ dones = dones.to(self.device)
+
+ valid = torch.ones_like(dones, dtype=torch.bool)
+ task_reward_weight, amp_reward_coef = self._apply_amp_reward_schedule()
+ amp_reward_weight = 1.0 - task_reward_weight
+ stage_weights = self._stage_weights(valid)
+ amp_rewards, amp_prior_rewards, amp_disc, amp_stage_logs = (
+ self.alg.predict_amp_reward(
+ amp_obs,
+ next_amp_obs,
+ task_rewards,
+ stage_weights=stage_weights,
+ )
+ )
+ amp_prior_rewards, style_gate = self._style_reward_gate(
+ amp_prior_rewards, dones
+ )
+ amp_rewards = (
+ task_reward_weight * task_rewards + amp_reward_weight * amp_prior_rewards
+ )
+ self.alg.process_amp_step(
+ amp_obs,
+ next_amp_obs,
+ stage_weights=stage_weights,
+ )
+
+ extras.setdefault("log", {})[f"Rewards/{log_namespace}/summary/task"] = (
+ task_rewards.mean()
+ )
+ task_component_mean = task_reward_weight * task_rewards.mean()
+ amp_component_mean = amp_reward_weight * amp_prior_rewards.mean()
+ extras["log"][f"Rewards/{log_namespace}/summary/style_prior"] = (
+ amp_prior_rewards.mean()
+ )
+ extras["log"][f"Rewards/{log_namespace}/summary/train_mixed"] = (
+ amp_rewards.mean()
+ )
+ extras["log"][f"Rewards/{log_namespace}/summary/style_to_task_abs"] = (
+ torch.abs(amp_component_mean)
+ / torch.abs(task_component_mean).clamp_min(1.0e-6)
+ )
+ if style_gate is not None:
+ extras["log"][f"Metrics/{log_namespace}/style_reward_gate_fraction"] = (
+ style_gate.mean()
+ )
+ extras["log"][f"Curriculum/{log_namespace}/task_reward_lerp"] = (
+ torch.as_tensor(
+ task_reward_weight,
+ dtype=task_rewards.dtype,
+ device=task_rewards.device,
+ )
+ )
+ extras["log"][f"Curriculum/{log_namespace}/reward_coef"] = torch.as_tensor(
+ amp_reward_coef,
+ dtype=task_rewards.dtype,
+ device=task_rewards.device,
+ )
+ if getattr(self.alg, "uses_stage_amp", False):
+ for name, replay in self.alg.stage_replay_buffers.items():
+ extras["log"][f"Metrics/{log_namespace}/amp/{name}_replay_size"] = (
+ torch.as_tensor(
+ replay.size,
+ dtype=task_rewards.dtype,
+ device=task_rewards.device,
+ )
+ )
+ else:
+ replay = getattr(self.alg, "amp_replay", None)
+ if replay is not None:
+ extras["log"][f"Metrics/{log_namespace}/amp/replay_size"] = (
+ torch.as_tensor(
+ replay.size,
+ dtype=task_rewards.dtype,
+ device=task_rewards.device,
+ )
+ )
+ if verbose_log:
+ task_reward_rate, task_dt_scale = self._task_reward_rate(task_rewards)
+ style_ceiling = self._style_reward_ceiling(task_rewards, stage_weights)
+ amp_ceiling_component_mean = amp_reward_weight * style_ceiling.mean()
+ extras["log"][
+ f"RewardsDebug/{log_namespace}/summary/task_rate_unscaled"
+ ] = task_reward_rate.mean()
+ extras["log"][f"RewardsDebug/{log_namespace}/components/task"] = (
+ task_component_mean
+ )
+ extras["log"][f"RewardsDebug/{log_namespace}/components/style"] = (
+ amp_component_mean
+ )
+ extras["log"][f"RewardsDebug/{log_namespace}/components/style_ceiling"] = (
+ amp_ceiling_component_mean
+ )
+ extras["log"][
+ f"RewardsDebug/{log_namespace}/ratios/style_ceiling_to_task_abs"
+ ] = torch.abs(amp_ceiling_component_mean) / torch.abs(
+ task_component_mean
+ ).clamp_min(1.0e-6)
+ extras["log"][
+ f"MetricsDebug/{log_namespace}/reward_scale/task_dt_scale"
+ ] = torch.as_tensor(
+ task_dt_scale,
+ dtype=task_rewards.dtype,
+ device=task_rewards.device,
+ )
+ extras["log"][
+ f"MetricsDebug/{log_namespace}/reward_scale/style_fraction_of_ceiling"
+ ] = amp_prior_rewards.mean() / style_ceiling.mean().clamp_min(1.0e-6)
+ extras["log"][f"RewardsDebug/{log_namespace}/amp/scale"] = torch.as_tensor(
+ style_ceiling.mean(),
+ dtype=task_rewards.dtype,
+ device=task_rewards.device,
+ )
+ extras["log"][f"MetricsDebug/{log_namespace}/amp/disc_pred_mean"] = (
+ amp_disc.mean()
+ )
+ extras["log"][f"MetricsDebug/{log_namespace}/amp/done_transition_ratio"] = (
+ dones.float().mean()
+ )
+ for key, value in amp_stage_logs.items():
+ extras["log"][f"MetricsDebug/{log_namespace}/amp/{key}"] = value.mean()
+
+ step_extras = dict(extras)
+ if "log" in extras:
+ step_extras["log"] = dict(extras["log"])
+ obs_after_reset = self._reset_done_envs(dones)
+ if obs_after_reset is not None:
+ obs = obs_after_reset
+
+ self.alg.process_env_step(obs, amp_rewards, dones, step_extras)
+ intrinsic_rewards = (
+ self.alg.intrinsic_rewards if self.cfg["algorithm"]["rnd_cfg"] else None
+ )
+ self.logger.process_env_step(
+ amp_rewards, dones, step_extras, intrinsic_rewards
+ )
+
+ stop = time.time()
+ collect_time = stop - start
+ start = stop
+ self.alg.compute_returns(obs)
+
+ loss_dict = self.alg.update()
+
+ stop = time.time()
+ learn_time = stop - start
+ self.current_learning_iteration = it
+ self.logger.log(
+ it=it,
+ start_it=start_it,
+ total_it=total_it,
+ collect_time=collect_time,
+ learn_time=learn_time,
+ loss_dict=loss_dict,
+ learning_rate=self.alg.learning_rate,
+ action_std=self.alg.get_policy().output_std,
+ rnd_weight=self.alg.rnd.weight if self.cfg["algorithm"]["rnd_cfg"] else None,
+ print_minimal=not verbose_log,
+ )
+
+ if self.logger.writer is not None and it % self.cfg["save_interval"] == 0:
+ self.save(os.path.join(self.logger.log_dir, f"model_{it}.pt")) # type: ignore[arg-type]
+
+ if self.logger.writer is not None:
+ self.save(
+ os.path.join(self.logger.log_dir, f"model_{self.current_learning_iteration}.pt")
+ ) # type: ignore[arg-type]
+ self.logger.stop_logging_writer()
diff --git a/src/mjlab_playground/amp/rl/runner_cfg.py b/src/mjlab_playground/amp/rl/runner_cfg.py
new file mode 100644
index 0000000..d5a6040
--- /dev/null
+++ b/src/mjlab_playground/amp/rl/runner_cfg.py
@@ -0,0 +1,22 @@
+"""Shared runner and algorithm configuration for AMP tasks."""
+
+# pyright: reportIncompatibleVariableOverride=false
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+from mjlab.rl import RslRlOnPolicyRunnerCfg, RslRlPpoAlgorithmCfg
+
+
+@dataclass
+class AmpPpoAlgorithmCfg(RslRlPpoAlgorithmCfg):
+ class_name: str = "mjlab_playground.amp.rl.amp_ppo:AmpPPO"
+ amp_cfg: dict = field(default_factory=dict)
+ symmetry_cfg: dict | None = None
+
+
+@dataclass
+class AmpOnPolicyRunnerCfg(RslRlOnPolicyRunnerCfg):
+ class_name: str = "mjlab_playground.amp.rl.runner:AmpOnPolicyRunner"
+ algorithm: AmpPpoAlgorithmCfg = field(default_factory=AmpPpoAlgorithmCfg)
diff --git a/src/mjlab_playground/asset_zoo/motions/retargeted/README.md b/src/mjlab_playground/asset_zoo/motions/retargeted/README.md
new file mode 100644
index 0000000..7add7ca
--- /dev/null
+++ b/src/mjlab_playground/asset_zoo/motions/retargeted/README.md
@@ -0,0 +1,5 @@
+# Local retargeted motions
+
+Keep locally generated motion artifacts below this directory only when a package-relative path is convenient. Git ignores the contents of this directory except for this README.
+
+Prefer setting `MJLAB_PLAYGROUND_MOTION_ROOT` or `MJLAB_TRACKING_MOTION_FILE` to a data directory outside the source tree. See the repository's `docs/motion_data.md` for the expected layout, schema, and redistribution policy.
diff --git a/src/mjlab_playground/asset_zoo/robots/booster_k1/__init__.py b/src/mjlab_playground/asset_zoo/robots/booster_k1/__init__.py
new file mode 100644
index 0000000..e63d5e7
--- /dev/null
+++ b/src/mjlab_playground/asset_zoo/robots/booster_k1/__init__.py
@@ -0,0 +1 @@
+"""Booster K1 humanoid."""
diff --git a/src/mjlab_playground/asset_zoo/robots/booster_k1/k1_constants.py b/src/mjlab_playground/asset_zoo/robots/booster_k1/k1_constants.py
new file mode 100644
index 0000000..46539b5
--- /dev/null
+++ b/src/mjlab_playground/asset_zoo/robots/booster_k1/k1_constants.py
@@ -0,0 +1,213 @@
+"""Booster K1 constants."""
+
+import os
+from pathlib import Path
+
+import mujoco
+from mjlab.actuator import BuiltinPositionActuatorCfg
+from mjlab.entity import EntityArticulationInfoCfg, EntityCfg
+
+##
+# MJCF and assets.
+##
+
+_PACKAGED_K1_XML = Path(__file__).parent / "xmls" / "k1" / "robot.xml"
+
+
+def _resolve_k1_xml() -> Path:
+ candidates = (
+ Path(os.environ["MJLAB_K1_XML"]) if "MJLAB_K1_XML" in os.environ else None,
+ _PACKAGED_K1_XML,
+ )
+ for path in candidates:
+ if path is not None and path.exists():
+ return path
+ checked = ", ".join(str(path) for path in candidates if path is not None)
+ raise FileNotFoundError(
+ "K1 MJCF robot.xml was not found. Set MJLAB_K1_XML or sync "
+ f"the packaged K1 xmls directory. Checked: {checked}"
+ )
+
+
+def get_spec() -> mujoco.MjSpec:
+ xml_path = _resolve_k1_xml()
+ xml = xml_path.read_text()
+ xml = xml.replace('meshdir="k1/meshes/"', 'meshdir=""')
+ meshes_dir = xml_path.parent / "meshes"
+ assets = {
+ path.name: path.read_bytes() for path in meshes_dir.glob("*") if path.is_file()
+ }
+ spec = mujoco.MjSpec.from_string(xml, assets=assets)
+ while spec.actuators:
+ spec.delete(spec.actuators[0])
+ return spec
+
+
+##
+# Actuator config.
+##
+
+
+K1_JOINT_NAMES: tuple[str, ...] = (
+ "AAHead_yaw",
+ "Head_pitch",
+ "ALeft_Shoulder_Pitch",
+ "Left_Shoulder_Roll",
+ "Left_Elbow_Pitch",
+ "Left_Elbow_Yaw",
+ "ARight_Shoulder_Pitch",
+ "Right_Shoulder_Roll",
+ "Right_Elbow_Pitch",
+ "Right_Elbow_Yaw",
+ "Left_Hip_Pitch",
+ "Left_Hip_Roll",
+ "Left_Hip_Yaw",
+ "Left_Knee_Pitch",
+ "Left_Ankle_Pitch",
+ "Left_Ankle_Roll",
+ "Right_Hip_Pitch",
+ "Right_Hip_Roll",
+ "Right_Hip_Yaw",
+ "Right_Knee_Pitch",
+ "Right_Ankle_Pitch",
+ "Right_Ankle_Roll",
+)
+
+
+def _kp(armature: float, natural_freq: float) -> float:
+ return armature * (2 * 3.1415926535 * natural_freq) ** 2
+
+
+def _kv(armature: float, natural_freq: float, damping_ratio: float) -> float:
+ return 2.0 * damping_ratio * armature * (2 * 3.1415926535 * natural_freq)
+
+
+_NF = 4.0
+
+K1_ACTUATOR_HEAD = BuiltinPositionActuatorCfg(
+ target_names_expr=("AAHead_yaw", "Head_pitch"),
+ stiffness=_kp(0.001, _NF),
+ damping=_kv(0.001, _NF, 2.0),
+ effort_limit=6.0,
+ armature=0.001,
+)
+
+K1_ACTUATOR_ARM = BuiltinPositionActuatorCfg(
+ target_names_expr=(
+ "ALeft_Shoulder_Pitch",
+ "Left_Shoulder_Roll",
+ "Left_Elbow_Pitch",
+ "Left_Elbow_Yaw",
+ "ARight_Shoulder_Pitch",
+ "Right_Shoulder_Roll",
+ "Right_Elbow_Pitch",
+ "Right_Elbow_Yaw",
+ ),
+ stiffness=_kp(0.001, _NF),
+ damping=_kv(0.001, _NF, 2.0),
+ effort_limit=14.0,
+ armature=0.001,
+)
+
+K1_ACTUATOR_HIP_PITCH = BuiltinPositionActuatorCfg(
+ target_names_expr=(".*_Hip_Pitch",),
+ stiffness=_kp(0.0478125, _NF),
+ damping=_kv(0.0478125, _NF, 1.5),
+ effort_limit=68.0,
+ armature=0.0478125,
+)
+
+K1_ACTUATOR_HIP_ROLL = BuiltinPositionActuatorCfg(
+ target_names_expr=(".*_Hip_Roll",),
+ stiffness=_kp(0.0339552, _NF),
+ damping=_kv(0.0339552, _NF, 1.5),
+ effort_limit=76.0,
+ armature=0.0339552,
+)
+
+K1_ACTUATOR_HIP_YAW = BuiltinPositionActuatorCfg(
+ target_names_expr=(".*_Hip_Yaw",),
+ stiffness=_kp(0.0282528, _NF),
+ damping=_kv(0.0282528, _NF, 1.5),
+ effort_limit=38.3,
+ armature=0.0282528,
+)
+
+K1_ACTUATOR_KNEE = BuiltinPositionActuatorCfg(
+ target_names_expr=(".*_Knee_Pitch",),
+ stiffness=_kp(0.095625, _NF),
+ damping=_kv(0.095625, _NF, 1.0),
+ effort_limit=112.0,
+ armature=0.095625,
+)
+
+_ANKLE_ARMATURE = 0.0282528 * 2.0
+
+K1_ACTUATOR_ANKLE_PITCH = BuiltinPositionActuatorCfg(
+ target_names_expr=(".*_Ankle_Pitch",),
+ stiffness=_kp(_ANKLE_ARMATURE, _NF),
+ damping=_kv(_ANKLE_ARMATURE, _NF, 1.5),
+ effort_limit=38.3,
+ armature=_ANKLE_ARMATURE,
+)
+
+K1_ACTUATOR_ANKLE_ROLL = BuiltinPositionActuatorCfg(
+ target_names_expr=(".*_Ankle_Roll",),
+ stiffness=_kp(_ANKLE_ARMATURE, _NF),
+ damping=_kv(_ANKLE_ARMATURE, _NF, 1.5),
+ effort_limit=38.3,
+ armature=_ANKLE_ARMATURE,
+)
+
+##
+# Keyframes.
+##
+
+HOME_KEYFRAME = EntityCfg.InitialStateCfg(
+ pos=(0.0, 0.0, 0.543),
+ joint_pos={
+ "Left_Shoulder_Roll": -1.3,
+ "Left_Elbow_Yaw": -0.4,
+ "Right_Shoulder_Roll": 1.3,
+ "Right_Elbow_Yaw": 0.4,
+ ".*_Hip_Pitch": -0.2,
+ ".*_Knee_Pitch": 0.4,
+ ".*_Ankle_Pitch": -0.2,
+ },
+ joint_vel={".*": 0.0},
+)
+
+##
+# Final config.
+##
+
+K1_ARTICULATION = EntityArticulationInfoCfg(
+ actuators=(
+ K1_ACTUATOR_HEAD,
+ K1_ACTUATOR_ARM,
+ K1_ACTUATOR_HIP_PITCH,
+ K1_ACTUATOR_HIP_ROLL,
+ K1_ACTUATOR_HIP_YAW,
+ K1_ACTUATOR_KNEE,
+ K1_ACTUATOR_ANKLE_PITCH,
+ K1_ACTUATOR_ANKLE_ROLL,
+ ),
+ soft_joint_pos_limit_factor=0.9,
+)
+
+
+def get_k1_robot_cfg() -> EntityCfg:
+ """Get a fresh K1 robot configuration instance."""
+ return EntityCfg(
+ init_state=HOME_KEYFRAME,
+ spec_fn=get_spec,
+ articulation=K1_ARTICULATION,
+ )
+
+
+K1_ACTION_SCALE: dict[str, float] = {}
+for a in K1_ARTICULATION.actuators:
+ assert isinstance(a, BuiltinPositionActuatorCfg)
+ assert a.effort_limit is not None
+ for n in a.target_names_expr:
+ K1_ACTION_SCALE[n] = 0.25 * a.effort_limit / a.stiffness
diff --git a/src/mjlab_playground/asset_zoo/robots/booster_k1/xmls/k1/meshes/Ankle_Cross.STL b/src/mjlab_playground/asset_zoo/robots/booster_k1/xmls/k1/meshes/Ankle_Cross.STL
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diff --git a/src/mjlab_playground/asset_zoo/robots/booster_k1/xmls/scene_getup.xml b/src/mjlab_playground/asset_zoo/robots/booster_k1/xmls/scene_getup.xml
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+
+
+
+
+
+
+
+
diff --git a/src/mjlab_playground/asset_zoo/robots/booster_t1/t1_constants.py b/src/mjlab_playground/asset_zoo/robots/booster_t1/t1_constants.py
index 6a3bc54..c13bd01 100644
--- a/src/mjlab_playground/asset_zoo/robots/booster_t1/t1_constants.py
+++ b/src/mjlab_playground/asset_zoo/robots/booster_t1/t1_constants.py
@@ -1,5 +1,16 @@
-"""Booster T1 constants."""
+"""Booster T1 constants.
+This profile keeps the mjlab position-actuator control law but aligns the
+actuated joint limits/passive terms with the active competition-side T1:
+- joint armature: 0.01
+- joint viscous damping: 0.01 for the competition-collision profile
+- joint frictionloss: 0.1
+- actuator effort limits from the competition robot joint classes
+"""
+
+# pyright: reportAttributeAccessIssue=false
+
+from copy import deepcopy
from pathlib import Path
import mujoco
@@ -36,35 +47,70 @@ def get_spec() -> mujoco.MjSpec:
ARM_ACTUATOR = ElectricActuator(
reflected_inertia=reflected_inertia(21.8e-6, 36),
velocity_limit=_rpm(89),
- effort_limit=36.0,
+ effort_limit=18.0,
)
WAIST_HIP_ROLL_YAW_ACTUATOR = ElectricActuator(
reflected_inertia=reflected_inertia(76.5e-6, 25),
velocity_limit=_rpm(70),
- effort_limit=40.0,
+ effort_limit=30.0,
)
HIP_PITCH_ACTUATOR = ElectricActuator(
reflected_inertia=reflected_inertia(161.7e-6, 18),
velocity_limit=_rpm(157),
- effort_limit=55.0,
+ effort_limit=45.0,
)
KNEE_ACTUATOR = ElectricActuator(
reflected_inertia=reflected_inertia(196.3e-6, 18),
velocity_limit=_rpm(140),
- effort_limit=65.0,
+ effort_limit=60.0,
+)
+
+ANKLE_PITCH_ACTUATOR = ElectricActuator(
+ reflected_inertia=reflected_inertia(26.2e-6, 36),
+ velocity_limit=_rpm(117),
+ effort_limit=20.0,
)
-ANKLE_ACTUATOR = ElectricActuator(
+ANKLE_ROLL_ACTUATOR = ElectricActuator(
reflected_inertia=reflected_inertia(26.2e-6, 36),
velocity_limit=_rpm(117),
- effort_limit=50.0,
+ effort_limit=15.0,
)
NATURAL_FREQ = 5.0 * 2.0 * 3.14159265 # 5 Hz
DAMPING_RATIO = 2.0
+COMPETITION_JOINT_ARMATURE = 0.01
+COMPETITION_JOINT_FRICTIONLOSS = 0.1
+COMPETITION_JOINT_VISCOUS_DAMPING = 0.01
+
+T1_JOINT_NAMES: tuple[str, ...] = (
+ "AAHead_yaw",
+ "Head_pitch",
+ "Left_Shoulder_Pitch",
+ "Left_Shoulder_Roll",
+ "Left_Elbow_Pitch",
+ "Left_Elbow_Yaw",
+ "Right_Shoulder_Pitch",
+ "Right_Shoulder_Roll",
+ "Right_Elbow_Pitch",
+ "Right_Elbow_Yaw",
+ "Waist",
+ "Left_Hip_Pitch",
+ "Left_Hip_Roll",
+ "Left_Hip_Yaw",
+ "Left_Knee_Pitch",
+ "Left_Ankle_Pitch",
+ "Left_Ankle_Roll",
+ "Right_Hip_Pitch",
+ "Right_Hip_Roll",
+ "Right_Hip_Yaw",
+ "Right_Knee_Pitch",
+ "Right_Ankle_Pitch",
+ "Right_Ankle_Roll",
+)
def _kp(act: ElectricActuator) -> float:
@@ -80,7 +126,8 @@ def _kv(act: ElectricActuator) -> float:
stiffness=_kp(NECK_ACTUATOR),
damping=_kv(NECK_ACTUATOR),
effort_limit=NECK_ACTUATOR.effort_limit,
- armature=NECK_ACTUATOR.reflected_inertia,
+ armature=COMPETITION_JOINT_ARMATURE,
+ frictionloss=COMPETITION_JOINT_FRICTIONLOSS,
)
T1_ACTUATOR_ARM = BuiltinPositionActuatorCfg(
@@ -93,7 +140,8 @@ def _kv(act: ElectricActuator) -> float:
stiffness=_kp(ARM_ACTUATOR),
damping=_kv(ARM_ACTUATOR),
effort_limit=ARM_ACTUATOR.effort_limit,
- armature=ARM_ACTUATOR.reflected_inertia,
+ armature=COMPETITION_JOINT_ARMATURE,
+ frictionloss=COMPETITION_JOINT_FRICTIONLOSS,
)
T1_ACTUATOR_WAIST_HIP_ROLL_YAW = BuiltinPositionActuatorCfg(
@@ -101,7 +149,8 @@ def _kv(act: ElectricActuator) -> float:
stiffness=_kp(WAIST_HIP_ROLL_YAW_ACTUATOR),
damping=_kv(WAIST_HIP_ROLL_YAW_ACTUATOR),
effort_limit=WAIST_HIP_ROLL_YAW_ACTUATOR.effort_limit,
- armature=WAIST_HIP_ROLL_YAW_ACTUATOR.reflected_inertia,
+ armature=COMPETITION_JOINT_ARMATURE,
+ frictionloss=COMPETITION_JOINT_FRICTIONLOSS,
)
T1_ACTUATOR_HIP_PITCH = BuiltinPositionActuatorCfg(
@@ -109,7 +158,8 @@ def _kv(act: ElectricActuator) -> float:
stiffness=_kp(HIP_PITCH_ACTUATOR),
damping=_kv(HIP_PITCH_ACTUATOR),
effort_limit=HIP_PITCH_ACTUATOR.effort_limit,
- armature=HIP_PITCH_ACTUATOR.reflected_inertia,
+ armature=COMPETITION_JOINT_ARMATURE,
+ frictionloss=COMPETITION_JOINT_FRICTIONLOSS,
)
T1_ACTUATOR_KNEE = BuiltinPositionActuatorCfg(
@@ -117,15 +167,26 @@ def _kv(act: ElectricActuator) -> float:
stiffness=_kp(KNEE_ACTUATOR),
damping=_kv(KNEE_ACTUATOR),
effort_limit=KNEE_ACTUATOR.effort_limit,
- armature=KNEE_ACTUATOR.reflected_inertia,
+ armature=COMPETITION_JOINT_ARMATURE,
+ frictionloss=COMPETITION_JOINT_FRICTIONLOSS,
+)
+
+T1_ACTUATOR_ANKLE_PITCH = BuiltinPositionActuatorCfg(
+ target_names_expr=(".*_Ankle_Pitch",),
+ stiffness=_kp(ANKLE_PITCH_ACTUATOR),
+ damping=_kv(ANKLE_PITCH_ACTUATOR),
+ effort_limit=ANKLE_PITCH_ACTUATOR.effort_limit,
+ armature=COMPETITION_JOINT_ARMATURE,
+ frictionloss=COMPETITION_JOINT_FRICTIONLOSS,
)
-T1_ACTUATOR_ANKLE = BuiltinPositionActuatorCfg(
- target_names_expr=(".*_Ankle_Pitch", ".*_Ankle_Roll"),
- stiffness=_kp(ANKLE_ACTUATOR),
- damping=_kv(ANKLE_ACTUATOR),
- effort_limit=ANKLE_ACTUATOR.effort_limit,
- armature=ANKLE_ACTUATOR.reflected_inertia,
+T1_ACTUATOR_ANKLE_ROLL = BuiltinPositionActuatorCfg(
+ target_names_expr=(".*_Ankle_Roll",),
+ stiffness=_kp(ANKLE_ROLL_ACTUATOR),
+ damping=_kv(ANKLE_ROLL_ACTUATOR),
+ effort_limit=ANKLE_ROLL_ACTUATOR.effort_limit,
+ armature=COMPETITION_JOINT_ARMATURE,
+ frictionloss=COMPETITION_JOINT_FRICTIONLOSS,
)
##
@@ -151,6 +212,26 @@ def _kv(act: ElectricActuator) -> float:
##
_foot_regex = r"^(left|right)_foot\d+_collision$"
+_competition_foot_regex = r"^(left|right)_foot$"
+_competition_foot_friction = (1.0, 0.01, 0.005)
+_competition_foot_solref = (0.02, 1.0)
+_competition_foot_solimp = (0.015, 1.0, 0.015, 0.5, 2.0)
+_competition_collision_regex = (
+ r"^(torso|head|left_forearm|left_hand|right_forearm|right_hand|"
+ r"left_thigh|left_calf|left_foot|right_thigh|right_calf|right_foot)$"
+)
+_competition_collision_friction = (1.0, 0.01, 0.005)
+_competition_collision_solref = (0.02, 1.0)
+_competition_collision_solimp = (0.9, 0.95, 0.001, 0.5, 2.0)
+_competition_collision_rgba = (0.0, 1.0, 0.0, 0.0)
+_competition_foot_inertia = (0.00268212, 0.002385, 0.000726885)
+_competition_foot_iquat = (0.0, 0.6799274021193834, 0.0, 0.7332794336725845)
+_competition_contact_excludes = (
+ ("Hip_Roll_Left", "Shank_Left"),
+ ("Shank_Left", "left_foot_link"),
+ ("Hip_Roll_Right", "Shank_Right"),
+ ("Shank_Right", "right_foot_link"),
+)
FULL_COLLISION = CollisionCfg(
geom_names_expr=(".*_collision",),
@@ -160,6 +241,30 @@ def _kv(act: ElectricActuator) -> float:
priority=1,
)
+COMPETITION_FOOT_COLLISION = CollisionCfg(
+ geom_names_expr=(".*_collision", _competition_foot_regex),
+ solref={_competition_foot_regex: _competition_foot_solref, ".*_collision": (0.01, 1)},
+ solimp={_competition_foot_regex: _competition_foot_solimp},
+ condim={_competition_foot_regex: 3, ".*_collision": 3},
+ friction={
+ _competition_foot_regex: _competition_foot_friction,
+ ".*_collision": (0.6,),
+ },
+ priority=1,
+)
+
+COMPETITION_COLLISION = CollisionCfg(
+ geom_names_expr=(_competition_collision_regex,),
+ solref=_competition_collision_solref,
+ solimp={
+ _competition_foot_regex: _competition_foot_solimp,
+ _competition_collision_regex: _competition_collision_solimp,
+ },
+ condim=3,
+ friction=_competition_collision_friction,
+ priority=1,
+)
+
##
# Final config.
##
@@ -171,7 +276,8 @@ def _kv(act: ElectricActuator) -> float:
T1_ACTUATOR_WAIST_HIP_ROLL_YAW,
T1_ACTUATOR_HIP_PITCH,
T1_ACTUATOR_KNEE,
- T1_ACTUATOR_ANKLE,
+ T1_ACTUATOR_ANKLE_PITCH,
+ T1_ACTUATOR_ANKLE_ROLL,
),
soft_joint_pos_limit_factor=0.9,
)
@@ -187,6 +293,248 @@ def get_t1_robot_cfg() -> EntityCfg:
)
+def get_competition_foot_spec() -> mujoco.MjSpec:
+ """Get the T1 spec with competition-style box foot collision geoms.
+
+ This keeps the training-side T1 meshes, inertials, joints, and actuators, but
+ replaces the four capsule foot collision geoms per foot with one box collision
+ geom per foot matching the competition T1 foot shape.
+ """
+ spec = get_spec()
+ capsule_foot_names = {
+ f"{side}_foot{idx}_collision" for side in ("left", "right") for idx in range(1, 5)
+ }
+ for geom in tuple(spec.geoms):
+ if geom.name in capsule_foot_names:
+ spec.delete(geom)
+
+ for side in ("left", "right"):
+ body = spec.body(f"{side}_foot_link")
+ body.add_geom(
+ name=f"{side}_foot",
+ type=mujoco.mjtGeom.mjGEOM_BOX,
+ pos=(0.01, 0.0, -0.015),
+ size=(0.1115, 0.05, 0.015),
+ contype=1,
+ conaffinity=1,
+ condim=3,
+ priority=1,
+ friction=_competition_foot_friction,
+ solref=_competition_foot_solref,
+ solimp=_competition_foot_solimp,
+ rgba=(0.0, 1.0, 0.0, 0.5),
+ )
+ return spec
+
+
+_T1_TRAINING_COLLISION_GEOM_NAMES = {
+ "trunk_collision",
+ "head_collision",
+ "left_shoulder_collision",
+ "left_upper_arm_collision",
+ "left_elbow_collision",
+ "left_lower_arm_collision",
+ "right_shoulder_collision",
+ "right_upper_arm_collision",
+ "right_elbow_collision",
+ "right_lower_arm_collision",
+ "waist_collision",
+ "left_thigh_collision",
+ "left_shin_collision",
+ "left_knee_collision",
+ "right_thigh_collision",
+ "right_shin_collision",
+ "right_knee_collision",
+ *{f"{side}_foot{idx}_collision" for side in ("left", "right") for idx in range(1, 5)},
+}
+
+
+def _delete_named_geoms(spec: mujoco.MjSpec, geom_names: set[str]) -> None:
+ for geom in tuple(spec.geoms):
+ if geom.name in geom_names:
+ spec.delete(geom)
+
+
+def _add_competition_collision_geom(
+ spec: mujoco.MjSpec,
+ *,
+ body_name: str,
+ geom_name: str,
+ geom_type: mujoco.mjtGeom,
+ pos: tuple[float, float, float],
+ size: tuple[float, float, float],
+ quat: tuple[float, float, float, float] = (1.0, 0.0, 0.0, 0.0),
+ solimp: tuple[float, float, float, float, float] = _competition_collision_solimp,
+) -> None:
+ spec.body(body_name).add_geom(
+ name=geom_name,
+ type=geom_type,
+ pos=pos,
+ size=size,
+ quat=quat,
+ contype=1,
+ conaffinity=1,
+ condim=3,
+ group=3,
+ priority=1,
+ friction=_competition_collision_friction,
+ solref=_competition_collision_solref,
+ solimp=solimp,
+ rgba=_competition_collision_rgba,
+ )
+
+
+def get_competition_collision_spec() -> mujoco.MjSpec:
+ """Get the T1 spec with competition-style collision geoms.
+
+ The source remains the training T1 MJCF so body/joint names used by the
+ mjlab task stay stable. Only collision geoms and the foot inertial frame are
+ aligned to the active competition T1 XML.
+ """
+ spec = get_spec()
+ _delete_named_geoms(spec, _T1_TRAINING_COLLISION_GEOM_NAMES)
+
+ # The competition server excludes different non-adjacent self-contact pairs
+ # than the original training MJCF. These pairs materially change leg motion.
+ for exclude in tuple(spec.excludes):
+ spec.delete(exclude)
+ for body_name_1, body_name_2 in _competition_contact_excludes:
+ spec.add_exclude(bodyname1=body_name_1, bodyname2=body_name_2)
+
+ for side in ("left", "right"):
+ foot_body = spec.body(f"{side}_foot_link")
+ foot_body.inertia = _competition_foot_inertia
+ foot_body.iquat = _competition_foot_iquat
+
+ _add_competition_collision_geom(
+ spec,
+ body_name="Trunk",
+ geom_name="torso",
+ geom_type=mujoco.mjtGeom.mjGEOM_BOX,
+ pos=(0.06, 0.0, 0.12),
+ size=(0.075, 0.1, 0.15),
+ )
+ _add_competition_collision_geom(
+ spec,
+ body_name="H2",
+ geom_name="head",
+ geom_type=mujoco.mjtGeom.mjGEOM_SPHERE,
+ pos=(0.01, 0.0, 0.11),
+ size=(0.08, 0.0, 0.0),
+ )
+ _add_competition_collision_geom(
+ spec,
+ body_name="AL3",
+ geom_name="left_forearm",
+ geom_type=mujoco.mjtGeom.mjGEOM_CAPSULE,
+ pos=(0.0, 0.05, 0.0),
+ size=(0.03, 0.075, 0.0),
+ quat=(0.70710678, 0.70710678, 0.0, 0.0),
+ )
+ _add_competition_collision_geom(
+ spec,
+ body_name="left_hand_link",
+ geom_name="left_hand",
+ geom_type=mujoco.mjtGeom.mjGEOM_CAPSULE,
+ pos=(0.0, 0.13, 0.0),
+ size=(0.03, 0.0875, 0.0),
+ quat=(0.70710678, 0.70710678, 0.0, 0.0),
+ )
+ _add_competition_collision_geom(
+ spec,
+ body_name="AR3",
+ geom_name="right_forearm",
+ geom_type=mujoco.mjtGeom.mjGEOM_CAPSULE,
+ pos=(0.0, -0.05, 0.0),
+ size=(0.03, 0.075, 0.0),
+ quat=(0.70710678, 0.70710678, 0.0, 0.0),
+ )
+ _add_competition_collision_geom(
+ spec,
+ body_name="right_hand_link",
+ geom_name="right_hand",
+ geom_type=mujoco.mjtGeom.mjGEOM_CAPSULE,
+ pos=(0.0, -0.13, 0.0),
+ size=(0.03, 0.0875, 0.0),
+ quat=(0.70710678, 0.70710678, 0.0, 0.0),
+ )
+ _add_competition_collision_geom(
+ spec,
+ body_name="Hip_Roll_Left",
+ geom_name="left_thigh",
+ geom_type=mujoco.mjtGeom.mjGEOM_CAPSULE,
+ pos=(0.0, 0.0, -0.08),
+ size=(0.05, 0.08, 0.0),
+ )
+ _add_competition_collision_geom(
+ spec,
+ body_name="Shank_Left",
+ geom_name="left_calf",
+ geom_type=mujoco.mjtGeom.mjGEOM_CAPSULE,
+ pos=(0.0, 0.0, -0.12),
+ size=(0.05, 0.075, 0.0),
+ )
+ _add_competition_collision_geom(
+ spec,
+ body_name="left_foot_link",
+ geom_name="left_foot",
+ geom_type=mujoco.mjtGeom.mjGEOM_BOX,
+ pos=(0.01, 0.0, -0.015),
+ size=(0.1115, 0.05, 0.015),
+ solimp=_competition_foot_solimp,
+ )
+ _add_competition_collision_geom(
+ spec,
+ body_name="Hip_Roll_Right",
+ geom_name="right_thigh",
+ geom_type=mujoco.mjtGeom.mjGEOM_CAPSULE,
+ pos=(0.0, 0.0, -0.08),
+ size=(0.05, 0.08, 0.0),
+ )
+ _add_competition_collision_geom(
+ spec,
+ body_name="Shank_Right",
+ geom_name="right_calf",
+ geom_type=mujoco.mjtGeom.mjGEOM_CAPSULE,
+ pos=(0.0, 0.0, -0.12),
+ size=(0.05, 0.075, 0.0),
+ )
+ _add_competition_collision_geom(
+ spec,
+ body_name="right_foot_link",
+ geom_name="right_foot",
+ geom_type=mujoco.mjtGeom.mjGEOM_BOX,
+ pos=(0.01, 0.0, -0.015),
+ size=(0.1115, 0.05, 0.015),
+ solimp=_competition_foot_solimp,
+ )
+ return spec
+
+
+def get_t1_competition_foot_robot_cfg() -> EntityCfg:
+ """Get a fresh T1 robot config with competition-style box foot contacts."""
+ return EntityCfg(
+ init_state=HOME_KEYFRAME,
+ collisions=(COMPETITION_FOOT_COLLISION,),
+ spec_fn=get_competition_foot_spec,
+ articulation=T1_ARTICULATION,
+ )
+
+
+def get_t1_competition_collision_robot_cfg() -> EntityCfg:
+ """Get a fresh T1 robot config with competition-style collision contacts."""
+ articulation = deepcopy(T1_ARTICULATION)
+ for actuator in articulation.actuators:
+ assert isinstance(actuator, BuiltinPositionActuatorCfg)
+ actuator.viscous_damping = COMPETITION_JOINT_VISCOUS_DAMPING
+ return EntityCfg(
+ init_state=HOME_KEYFRAME,
+ collisions=(COMPETITION_COLLISION,),
+ spec_fn=get_competition_collision_spec,
+ articulation=articulation,
+ )
+
+
T1_ACTION_SCALE: dict[str, float] = {}
for a in T1_ARTICULATION.actuators:
assert isinstance(a, BuiltinPositionActuatorCfg)
diff --git a/src/mjlab_playground/tasks/__init__.py b/src/mjlab_playground/tasks/__init__.py
new file mode 100644
index 0000000..171452e
--- /dev/null
+++ b/src/mjlab_playground/tasks/__init__.py
@@ -0,0 +1 @@
+"""Task definitions and registrations built on mjlab."""
diff --git a/src/mjlab_playground/tasks/common/__init__.py b/src/mjlab_playground/tasks/common/__init__.py
new file mode 100644
index 0000000..ffc31bb
--- /dev/null
+++ b/src/mjlab_playground/tasks/common/__init__.py
@@ -0,0 +1,5 @@
+"""Helpers shared by multiple playground tasks."""
+
+from .events import reset_joints_by_offset
+
+__all__ = ["reset_joints_by_offset"]
diff --git a/src/mjlab_playground/tasks/common/events.py b/src/mjlab_playground/tasks/common/events.py
new file mode 100644
index 0000000..e2e602e
--- /dev/null
+++ b/src/mjlab_playground/tasks/common/events.py
@@ -0,0 +1,58 @@
+"""Reset events shared by locomotion tasks."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import torch
+from mjlab.entity import Entity
+from mjlab.managers.scene_entity_config import SceneEntityCfg
+from mjlab.utils.lab_api.math import sample_uniform
+
+if TYPE_CHECKING:
+ from mjlab.envs import ManagerBasedRlEnv
+
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+
+
+def reset_joints_by_offset(
+ env: ManagerBasedRlEnv,
+ env_ids: torch.Tensor | None,
+ position_range: tuple[float, float],
+ velocity_range: tuple[float, float],
+ asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> None:
+ """Reset joints around their defaults for invariant or per-world limits."""
+ if env_ids is None:
+ env_ids = torch.arange(env.num_envs, device=env.device, dtype=torch.long)
+
+ asset: Entity = env.scene[asset_cfg.name]
+ default_joint_pos = asset.data.default_joint_pos
+ default_joint_vel = asset.data.default_joint_vel
+ soft_joint_pos_limits = asset.data.soft_joint_pos_limits
+ assert default_joint_pos is not None
+ assert default_joint_vel is not None
+ assert soft_joint_pos_limits is not None
+
+ joint_pos = default_joint_pos[env_ids][:, asset_cfg.joint_ids].clone()
+ joint_pos += sample_uniform(*position_range, joint_pos.shape, env.device)
+ limits = (
+ soft_joint_pos_limits.expand(len(env_ids), -1, -1)
+ if soft_joint_pos_limits.shape[0] == 1
+ else soft_joint_pos_limits[env_ids]
+ )
+ limits = limits[:, asset_cfg.joint_ids]
+ joint_pos.clamp_(limits[..., 0], limits[..., 1])
+
+ joint_vel = default_joint_vel[env_ids][:, asset_cfg.joint_ids].clone()
+ joint_vel += sample_uniform(*velocity_range, joint_vel.shape, env.device)
+
+ joint_ids = asset_cfg.joint_ids
+ if isinstance(joint_ids, list):
+ joint_ids = torch.tensor(joint_ids, device=env.device)
+ asset.write_joint_state_to_sim(
+ joint_pos.view(len(env_ids), -1),
+ joint_vel.view(len(env_ids), -1),
+ env_ids=env_ids,
+ joint_ids=joint_ids,
+ )
diff --git a/src/mjlab_playground/getup/__init__.py b/src/mjlab_playground/tasks/getup/__init__.py
similarity index 100%
rename from src/mjlab_playground/getup/__init__.py
rename to src/mjlab_playground/tasks/getup/__init__.py
diff --git a/src/mjlab_playground/getup/config/__init__.py b/src/mjlab_playground/tasks/getup/config/__init__.py
similarity index 100%
rename from src/mjlab_playground/getup/config/__init__.py
rename to src/mjlab_playground/tasks/getup/config/__init__.py
diff --git a/src/mjlab_playground/getup/config/go1/__init__.py b/src/mjlab_playground/tasks/getup/config/go1/__init__.py
similarity index 100%
rename from src/mjlab_playground/getup/config/go1/__init__.py
rename to src/mjlab_playground/tasks/getup/config/go1/__init__.py
diff --git a/src/mjlab_playground/getup/config/go1/env_cfgs.py b/src/mjlab_playground/tasks/getup/config/go1/env_cfgs.py
similarity index 96%
rename from src/mjlab_playground/getup/config/go1/env_cfgs.py
rename to src/mjlab_playground/tasks/getup/config/go1/env_cfgs.py
index af9d524..bd3988a 100644
--- a/src/mjlab_playground/getup/config/go1/env_cfgs.py
+++ b/src/mjlab_playground/tasks/getup/config/go1/env_cfgs.py
@@ -9,8 +9,8 @@
from mjlab.sensor import ContactMatch, ContactSensorCfg
from mjlab.utils.spec_config import CollisionCfg
-from mjlab_playground.getup import mdp
-from mjlab_playground.getup.getup_env_cfg import make_getup_env_cfg
+from mjlab_playground.tasks.getup import mdp
+from mjlab_playground.tasks.getup.getup_env_cfg import make_getup_env_cfg
_TORSO_HEIGHT = 0.275
diff --git a/src/mjlab_playground/getup/config/go1/rl_cfg.py b/src/mjlab_playground/tasks/getup/config/go1/rl_cfg.py
similarity index 100%
rename from src/mjlab_playground/getup/config/go1/rl_cfg.py
rename to src/mjlab_playground/tasks/getup/config/go1/rl_cfg.py
diff --git a/src/mjlab_playground/tasks/getup/config/k1/__init__.py b/src/mjlab_playground/tasks/getup/config/k1/__init__.py
new file mode 100644
index 0000000..d714032
--- /dev/null
+++ b/src/mjlab_playground/tasks/getup/config/k1/__init__.py
@@ -0,0 +1,11 @@
+from mjlab.tasks.registry import register_mjlab_task
+
+from .env_cfgs import booster_k1_getup_env_cfg
+from .rl_cfg import booster_k1_getup_ppo_runner_cfg
+
+register_mjlab_task(
+ task_id="Mjlab-Getup-Flat-Booster-K1",
+ env_cfg=booster_k1_getup_env_cfg(),
+ play_env_cfg=booster_k1_getup_env_cfg(play=True),
+ rl_cfg=booster_k1_getup_ppo_runner_cfg(),
+)
diff --git a/src/mjlab_playground/tasks/getup/config/k1/env_cfgs.py b/src/mjlab_playground/tasks/getup/config/k1/env_cfgs.py
new file mode 100644
index 0000000..c1d2751
--- /dev/null
+++ b/src/mjlab_playground/tasks/getup/config/k1/env_cfgs.py
@@ -0,0 +1,134 @@
+"""Booster K1 getup environment configuration."""
+
+from mjlab.envs import ManagerBasedRlEnvCfg
+from mjlab.managers.curriculum_manager import CurriculumTermCfg
+from mjlab.managers.reward_manager import RewardTermCfg
+from mjlab.managers.scene_entity_config import SceneEntityCfg
+from mjlab.sensor import ContactMatch, ContactSensorCfg
+
+from mjlab_playground.asset_zoo.robots.booster_k1.k1_constants import get_k1_robot_cfg
+from mjlab_playground.tasks.getup import mdp
+from mjlab_playground.tasks.getup.getup_env_cfg import make_getup_env_cfg
+from mjlab_playground.tasks.getup.mdp.actions import (
+ SettleRelativeJointPositionActionCfg,
+)
+
+# Derived from the K1 standing/getup calibration in the MotrixLab scene.
+_TORSO_HEIGHT = 0.543
+_HIP_HEIGHT = 0.466
+
+
+def booster_k1_getup_env_cfg(play: bool = False) -> ManagerBasedRlEnvCfg:
+ """Create Booster K1 getup task configuration."""
+ cfg = make_getup_env_cfg()
+
+ cfg.scene.entities = {"robot": get_k1_robot_cfg()}
+ cfg.episode_length_s = 8.0
+
+ cfg.observations["actor"].terms["base_ang_vel"].func = mdp.base_ang_vel
+ cfg.observations["actor"].terms["base_ang_vel"].params = {}
+ cfg.observations["critic"].terms["base_ang_vel"].func = mdp.base_ang_vel
+ cfg.observations["critic"].terms["base_ang_vel"].params = {}
+ cfg.observations["critic"].terms["base_lin_vel"].func = mdp.base_lin_vel
+ cfg.observations["critic"].terms["base_lin_vel"].params = {}
+
+ self_collision_cfg = ContactSensorCfg(
+ name="self_collision",
+ primary=ContactMatch(mode="subtree", pattern="torso", entity="robot"),
+ secondary=ContactMatch(mode="subtree", pattern="torso", entity="robot"),
+ fields=("found", "force"),
+ reduce="none",
+ num_slots=1,
+ history_length=4,
+ )
+ cfg.scene.sensors = (cfg.scene.sensors or ()) + (self_collision_cfg,)
+
+ cfg.rewards["self_collisions"] = RewardTermCfg(
+ func=mdp.self_collision_cost,
+ weight=-0.1,
+ params={"sensor_name": self_collision_cfg.name},
+ )
+
+ cfg.rewards["torso_height"].params["desired_height"] = _TORSO_HEIGHT
+ cfg.rewards["torso_height"].params["asset_cfg"] = SceneEntityCfg(
+ "robot", body_names=("torso",)
+ )
+ cfg.rewards["left_hip_height"] = RewardTermCfg(
+ func=mdp.height_reward,
+ weight=0.5,
+ params={
+ "desired_height": _HIP_HEIGHT,
+ "asset_cfg": SceneEntityCfg("robot", body_names=("Left_Hip_Pitch",)),
+ },
+ )
+ cfg.rewards["right_hip_height"] = RewardTermCfg(
+ func=mdp.height_reward,
+ weight=0.5,
+ params={
+ "desired_height": _HIP_HEIGHT,
+ "asset_cfg": SceneEntityCfg("robot", body_names=("Right_Hip_Pitch",)),
+ },
+ )
+ cfg.metrics["getup_success"].params["desired_height"] = _TORSO_HEIGHT
+
+ cfg.rewards["posture"].params["std"] = {
+ r".*_Hip_Roll": 0.08,
+ r".*_Hip_Yaw": 0.08,
+ r".*_Hip_Pitch": 0.12,
+ r".*_Knee_Pitch": 0.15,
+ r".*_Ankle_Pitch": 0.2,
+ r".*_Ankle_Roll": 0.2,
+ r"(AAHead_yaw|Head_pitch)": 0.15,
+ r"(.*_Shoulder.*|.*_Elbow.*)": 0.5,
+ }
+
+ cfg.viewer.body_name = "torso"
+
+ cfg.events["base_com"].params["asset_cfg"] = SceneEntityCfg(
+ "robot", body_names=("torso",)
+ )
+
+ cfg.events["reset_fallen_or_standing"].params["fall_height"] = 0.65
+
+ assert isinstance(cfg.actions["joint_pos"], SettleRelativeJointPositionActionCfg)
+ cfg.actions["joint_pos"].settle_steps = 50
+ cfg.terminations["energy"].params["settle_steps"] = 50
+ cfg.terminations["energy"].params["threshold"] = float("inf")
+ cfg.rewards["action_rate_l2"].weight = -0.005
+ cfg.rewards["joint_vel_l2"].weight = 0.0
+
+ # K1 has no waist joint, so supine recovery needs larger full-body swings for
+ # longer than T1. Keep the getup reward simple, but delay regularization.
+ cfg.curriculum = {
+ "action_rate_weight": CurriculumTermCfg(
+ func=mdp.reward_curriculum,
+ params={
+ "reward_name": "action_rate_l2",
+ "stages": [
+ {"step": 0, "weight": -0.005},
+ {"step": 1500 * 24, "weight": -0.01},
+ {"step": 2500 * 24, "weight": -0.03},
+ {"step": 3500 * 24, "weight": -0.05},
+ {"step": 4500 * 24, "weight": -0.08},
+ ],
+ },
+ ),
+ "joint_vel_weight": CurriculumTermCfg(
+ func=mdp.reward_curriculum,
+ params={
+ "reward_name": "joint_vel_l2",
+ "stages": [
+ {"step": 0, "weight": 0.0},
+ {"step": 2500 * 24, "weight": -0.002},
+ {"step": 3500 * 24, "weight": -0.005},
+ {"step": 4500 * 24, "weight": -0.008},
+ ],
+ },
+ ),
+ }
+
+ if play:
+ cfg.observations["actor"].enable_corruption = False
+ cfg.events["reset_fallen_or_standing"].params["fall_probability"] = 1.0
+
+ return cfg
diff --git a/src/mjlab_playground/tasks/getup/config/k1/rl_cfg.py b/src/mjlab_playground/tasks/getup/config/k1/rl_cfg.py
new file mode 100644
index 0000000..a580d8a
--- /dev/null
+++ b/src/mjlab_playground/tasks/getup/config/k1/rl_cfg.py
@@ -0,0 +1,47 @@
+"""RL configuration for Booster K1 getup task."""
+
+from mjlab.rl import (
+ RslRlModelCfg,
+ RslRlOnPolicyRunnerCfg,
+ RslRlPpoAlgorithmCfg,
+)
+
+
+def booster_k1_getup_ppo_runner_cfg() -> RslRlOnPolicyRunnerCfg:
+ """Create RL runner configuration for Booster K1 getup task."""
+ return RslRlOnPolicyRunnerCfg(
+ actor=RslRlModelCfg(
+ hidden_dims=(512, 256, 128),
+ activation="elu",
+ obs_normalization=False,
+ distribution_cfg={
+ "class_name": "GaussianDistribution",
+ "init_std": 1.0,
+ "std_type": "log",
+ },
+ ),
+ critic=RslRlModelCfg(
+ hidden_dims=(512, 256, 128),
+ activation="elu",
+ obs_normalization=True,
+ ),
+ algorithm=RslRlPpoAlgorithmCfg(
+ value_loss_coef=1.0,
+ use_clipped_value_loss=True,
+ clip_param=0.2,
+ entropy_coef=0.01,
+ num_learning_epochs=5,
+ num_mini_batches=4,
+ learning_rate=1.0e-3,
+ schedule="adaptive",
+ gamma=0.99,
+ lam=0.95,
+ desired_kl=0.01,
+ max_grad_norm=1.0,
+ ),
+ experiment_name="k1_getup",
+ wandb_project="mjlab_playground",
+ save_interval=50,
+ num_steps_per_env=24,
+ max_iterations=3_000,
+ )
diff --git a/src/mjlab_playground/getup/config/t1/__init__.py b/src/mjlab_playground/tasks/getup/config/t1/__init__.py
similarity index 100%
rename from src/mjlab_playground/getup/config/t1/__init__.py
rename to src/mjlab_playground/tasks/getup/config/t1/__init__.py
diff --git a/src/mjlab_playground/getup/config/t1/env_cfgs.py b/src/mjlab_playground/tasks/getup/config/t1/env_cfgs.py
similarity index 96%
rename from src/mjlab_playground/getup/config/t1/env_cfgs.py
rename to src/mjlab_playground/tasks/getup/config/t1/env_cfgs.py
index 87343ed..6594c8d 100644
--- a/src/mjlab_playground/getup/config/t1/env_cfgs.py
+++ b/src/mjlab_playground/tasks/getup/config/t1/env_cfgs.py
@@ -9,9 +9,11 @@
from mjlab.sensor import ContactMatch, ContactSensorCfg
from mjlab_playground.asset_zoo.robots.booster_t1.t1_constants import get_t1_robot_cfg
-from mjlab_playground.getup import mdp
-from mjlab_playground.getup.getup_env_cfg import make_getup_env_cfg
-from mjlab_playground.getup.mdp.actions import SettleRelativeJointPositionActionCfg
+from mjlab_playground.tasks.getup import mdp
+from mjlab_playground.tasks.getup.getup_env_cfg import make_getup_env_cfg
+from mjlab_playground.tasks.getup.mdp.actions import (
+ SettleRelativeJointPositionActionCfg,
+)
# Derived from home keyframe.
_TORSO_HEIGHT = 0.67
diff --git a/src/mjlab_playground/getup/config/t1/rl_cfg.py b/src/mjlab_playground/tasks/getup/config/t1/rl_cfg.py
similarity index 100%
rename from src/mjlab_playground/getup/config/t1/rl_cfg.py
rename to src/mjlab_playground/tasks/getup/config/t1/rl_cfg.py
diff --git a/src/mjlab_playground/getup/getup_env_cfg.py b/src/mjlab_playground/tasks/getup/getup_env_cfg.py
similarity index 97%
rename from src/mjlab_playground/getup/getup_env_cfg.py
rename to src/mjlab_playground/tasks/getup/getup_env_cfg.py
index 3265463..0fd5d1b 100644
--- a/src/mjlab_playground/getup/getup_env_cfg.py
+++ b/src/mjlab_playground/tasks/getup/getup_env_cfg.py
@@ -25,8 +25,10 @@
from mjlab.utils.noise import UniformNoiseCfg as Unoise
from mjlab.viewer import ViewerConfig
-from mjlab_playground.getup import mdp
-from mjlab_playground.getup.mdp.actions import SettleRelativeJointPositionActionCfg
+from mjlab_playground.tasks.getup import mdp
+from mjlab_playground.tasks.getup.mdp.actions import (
+ SettleRelativeJointPositionActionCfg,
+)
def make_getup_env_cfg() -> ManagerBasedRlEnvCfg:
diff --git a/src/mjlab_playground/getup/mdp/__init__.py b/src/mjlab_playground/tasks/getup/mdp/__init__.py
similarity index 100%
rename from src/mjlab_playground/getup/mdp/__init__.py
rename to src/mjlab_playground/tasks/getup/mdp/__init__.py
diff --git a/src/mjlab_playground/getup/mdp/actions.py b/src/mjlab_playground/tasks/getup/mdp/actions.py
similarity index 100%
rename from src/mjlab_playground/getup/mdp/actions.py
rename to src/mjlab_playground/tasks/getup/mdp/actions.py
diff --git a/src/mjlab_playground/getup/mdp/events.py b/src/mjlab_playground/tasks/getup/mdp/events.py
similarity index 93%
rename from src/mjlab_playground/getup/mdp/events.py
rename to src/mjlab_playground/tasks/getup/mdp/events.py
index c2eb4a3..fd42f69 100644
--- a/src/mjlab_playground/getup/mdp/events.py
+++ b/src/mjlab_playground/tasks/getup/mdp/events.py
@@ -91,7 +91,13 @@ def reset_fallen_or_standing(
asset.write_root_link_velocity_to_sim(velocities, env_ids=env_ids)
# Joint state.
- joint_limits = soft_joint_pos_limits[env_ids]
+ # MJLab stores invariant limits once, while per-world variants may expose
+ # one row per environment. Support both layouts.
+ joint_limits = (
+ soft_joint_pos_limits.expand(n, -1, -1)
+ if soft_joint_pos_limits.shape[0] == 1
+ else soft_joint_pos_limits[env_ids]
+ )
random_joint_pos = sample_uniform(
joint_limits[..., 0], joint_limits[..., 1], joint_limits[..., 0].shape, env.device
)
diff --git a/src/mjlab_playground/getup/mdp/rewards.py b/src/mjlab_playground/tasks/getup/mdp/rewards.py
similarity index 100%
rename from src/mjlab_playground/getup/mdp/rewards.py
rename to src/mjlab_playground/tasks/getup/mdp/rewards.py
diff --git a/src/mjlab_playground/getup/mdp/terminations.py b/src/mjlab_playground/tasks/getup/mdp/terminations.py
similarity index 100%
rename from src/mjlab_playground/getup/mdp/terminations.py
rename to src/mjlab_playground/tasks/getup/mdp/terminations.py
diff --git a/src/mjlab_playground/tasks/target_location_amp/__init__.py b/src/mjlab_playground/tasks/target_location_amp/__init__.py
new file mode 100644
index 0000000..2279123
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/__init__.py
@@ -0,0 +1 @@
+"""Target-location locomotion AMP tasks."""
diff --git a/src/mjlab_playground/tasks/target_location_amp/config/__init__.py b/src/mjlab_playground/tasks/target_location_amp/config/__init__.py
new file mode 100644
index 0000000..43c4884
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/config/__init__.py
@@ -0,0 +1 @@
+"""Target-location locomotion AMP configurations."""
diff --git a/src/mjlab_playground/tasks/target_location_amp/config/k1/__init__.py b/src/mjlab_playground/tasks/target_location_amp/config/k1/__init__.py
new file mode 100644
index 0000000..7eaa1c0
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/config/k1/__init__.py
@@ -0,0 +1,16 @@
+"""Booster K1 target-location AMP task registration."""
+
+from mjlab.tasks.registry import register_mjlab_task
+
+from mjlab_playground.amp.rl import AmpOnPolicyRunner
+
+from .env_cfgs import booster_k1_target_location_amp_flat_env_cfg
+from .rl_cfg import booster_k1_target_location_amp_runner_cfg
+
+register_mjlab_task(
+ task_id="Mjlab-TargetLocationAmp-Flat-Booster-K1",
+ env_cfg=booster_k1_target_location_amp_flat_env_cfg(),
+ play_env_cfg=booster_k1_target_location_amp_flat_env_cfg(play=True),
+ rl_cfg=booster_k1_target_location_amp_runner_cfg(),
+ runner_cls=AmpOnPolicyRunner,
+)
diff --git a/src/mjlab_playground/tasks/target_location_amp/config/k1/env_cfgs.py b/src/mjlab_playground/tasks/target_location_amp/config/k1/env_cfgs.py
new file mode 100644
index 0000000..ccf615e
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/config/k1/env_cfgs.py
@@ -0,0 +1,172 @@
+"""Booster K1 target-location AMP environment configuration."""
+
+from __future__ import annotations
+
+import math
+from copy import deepcopy
+
+from mjlab.envs import ManagerBasedRlEnvCfg
+from mjlab.envs.mdp.actions import RelativeJointPositionActionCfg
+from mjlab.managers.curriculum_manager import CurriculumTermCfg
+from mjlab.managers.scene_entity_config import SceneEntityCfg
+from mjlab.managers.termination_manager import TerminationTermCfg
+
+from mjlab_playground.asset_zoo.robots.booster_k1.k1_constants import (
+ get_k1_robot_cfg,
+)
+from mjlab_playground.tasks.target_location_amp import mdp as target_mdp
+from mjlab_playground.tasks.target_location_amp.target_location_amp_env_cfg import (
+ make_target_location_amp_env_cfg,
+)
+
+_K1_TARGET_POSITION_TOLERANCE = 0.16
+_K1_TARGET_YAW_TOLERANCE = 0.20
+_K1_TARGET_STABLE_HOLD_STEPS = 25
+_K1_TARGET_MAX_LIN_SPEED = 0.35
+_K1_TARGET_MAX_YAW_RATE = 0.60
+_K1_TARGET_MIN_UP_Z = 0.75
+_K1_EPISODE_LENGTH_S = 6.0
+_K1_FIXED_TARGET_RESAMPLING_TIME_RANGE = (1.0e9, 1.0e9)
+
+_K1_TARGET_LOCO_ACTION_SCALE = {
+ "AAHead_yaw": 0.5,
+ "Head_pitch": 0.5,
+ "ALeft_Shoulder_Pitch": 1.0,
+ "Left_Shoulder_Roll": 1.0,
+ "Left_Elbow_Pitch": 1.0,
+ "Left_Elbow_Yaw": 1.0,
+ "ARight_Shoulder_Pitch": 1.0,
+ "Right_Shoulder_Roll": 1.0,
+ "Right_Elbow_Pitch": 1.0,
+ "Right_Elbow_Yaw": 1.0,
+ ".*_Hip_Pitch": 0.9,
+ ".*_Hip_Roll": 0.8,
+ ".*_Hip_Yaw": 0.8,
+ ".*_Knee_Pitch": 1.0,
+ ".*_Ankle_Pitch": 0.6,
+ ".*_Ankle_Roll": 0.45,
+}
+
+_K1_TARGET_LOCO_GOAL_CURRICULUM_STAGES = [
+ {
+ "step": 0,
+ "target_radius": (0.4, 1.2),
+ "target_yaw": (-0.25, 0.25),
+ "target_angle": (-0.50, 0.50),
+ "resampling_time_range": None,
+ "resample_on_near_goal": False,
+ "resample_position_tolerance": _K1_TARGET_POSITION_TOLERANCE,
+ "resample_yaw_tolerance": _K1_TARGET_YAW_TOLERANCE,
+ },
+ {
+ "step": 600 * 24,
+ "target_radius": (0.4, 2.4),
+ "target_yaw": (-0.75, 0.75),
+ "target_angle": (-1.25, 1.25),
+ "resampling_time_range": None,
+ "resample_on_near_goal": False,
+ "resample_position_tolerance": _K1_TARGET_POSITION_TOLERANCE,
+ "resample_yaw_tolerance": _K1_TARGET_YAW_TOLERANCE,
+ },
+ {
+ "step": 1_600 * 24,
+ "target_radius": (0.4, 4.0),
+ "target_yaw": (-1.50, 1.50),
+ "target_angle": (-2.20, 2.20),
+ "resampling_time_range": None,
+ "resample_on_near_goal": False,
+ "resample_position_tolerance": _K1_TARGET_POSITION_TOLERANCE,
+ "resample_yaw_tolerance": _K1_TARGET_YAW_TOLERANCE,
+ },
+ {
+ "step": 3_500 * 24,
+ "target_radius": (0.4, 6.0),
+ "target_yaw": (-math.pi, math.pi),
+ "target_angle": (-math.pi, math.pi),
+ "resampling_time_range": None,
+ "resample_on_near_goal": False,
+ "resample_position_tolerance": _K1_TARGET_POSITION_TOLERANCE,
+ "resample_yaw_tolerance": _K1_TARGET_YAW_TOLERANCE,
+ },
+]
+
+
+def _apply_k1_joint_action_setup(cfg: ManagerBasedRlEnvCfg) -> None:
+ joint_action = cfg.actions["joint_pos"]
+ assert isinstance(joint_action, RelativeJointPositionActionCfg)
+ joint_action.scale = _K1_TARGET_LOCO_ACTION_SCALE
+ joint_action.clip = None
+
+
+def _robot_asset_cfg() -> SceneEntityCfg:
+ return SceneEntityCfg("robot", joint_names=(".*",))
+
+
+def _configure_k1_goal_reached_termination(cfg: ManagerBasedRlEnvCfg) -> None:
+ cfg.terminations["goal_reached"] = TerminationTermCfg(
+ func=target_mdp.goal_pose_reached,
+ params={
+ "command_name": "goal_pose",
+ "position_tolerance": _K1_TARGET_POSITION_TOLERANCE,
+ "yaw_tolerance": _K1_TARGET_YAW_TOLERANCE,
+ "hold_steps": _K1_TARGET_STABLE_HOLD_STEPS,
+ "entity_name": "robot",
+ "max_lin_speed": _K1_TARGET_MAX_LIN_SPEED,
+ "max_yaw_rate": _K1_TARGET_MAX_YAW_RATE,
+ "min_up_z": _K1_TARGET_MIN_UP_Z,
+ },
+ )
+
+
+def _configure_k1_goal_pose_command(cfg: ManagerBasedRlEnvCfg) -> None:
+ goal_pose_cmd = cfg.commands["goal_pose"]
+ assert isinstance(goal_pose_cmd, target_mdp.GoalPoseCommandCfg)
+ stage0 = _K1_TARGET_LOCO_GOAL_CURRICULUM_STAGES[0]
+ goal_pose_cmd.ranges.target_radius = stage0["target_radius"]
+ goal_pose_cmd.ranges.target_yaw = stage0["target_yaw"]
+ goal_pose_cmd.ranges.target_angle = stage0["target_angle"]
+ goal_pose_cmd.resampling_time_range = _K1_FIXED_TARGET_RESAMPLING_TIME_RANGE
+ goal_pose_cmd.resample_on_near_goal = stage0["resample_on_near_goal"]
+ goal_pose_cmd.resample_position_tolerance = stage0["resample_position_tolerance"]
+ goal_pose_cmd.resample_yaw_tolerance = stage0["resample_yaw_tolerance"]
+
+
+def booster_k1_target_location_amp_flat_env_cfg(
+ play: bool = False,
+) -> ManagerBasedRlEnvCfg:
+ """Create the K1 target-location locomotion AMP task."""
+ cfg = make_target_location_amp_env_cfg(play=play)
+ cfg.scene.entities = {
+ "robot": deepcopy(get_k1_robot_cfg()),
+ }
+ cfg.scene.num_envs = 1 if play else 1024
+ cfg.episode_length_s = _K1_EPISODE_LENGTH_S
+ cfg.viewer.body_name = "torso"
+ _apply_k1_joint_action_setup(cfg)
+ _configure_k1_goal_pose_command(cfg)
+ cfg.events["reset_robot_joints"].params["asset_cfg"] = _robot_asset_cfg()
+ cfg.observations["critic"].terms["amp_robot"].params["robot_cfg"] = _robot_asset_cfg()
+
+ if not play:
+ _configure_k1_goal_reached_termination(cfg)
+ cfg.curriculum["goal_pose"] = CurriculumTermCfg(
+ func=target_mdp.goal_pose_ranges,
+ params={
+ "command_name": "goal_pose",
+ "stages": _K1_TARGET_LOCO_GOAL_CURRICULUM_STAGES,
+ },
+ )
+
+ if play:
+ cfg.observations["actor"].enable_corruption = False
+ goal_pose_cmd = cfg.commands["goal_pose"]
+ assert isinstance(goal_pose_cmd, target_mdp.GoalPoseCommandCfg)
+ goal_pose_cmd.ranges.target_radius = (0.4, 6.0)
+ goal_pose_cmd.ranges.target_yaw = (-math.pi, math.pi)
+ goal_pose_cmd.ranges.target_angle = (-math.pi, math.pi)
+ goal_pose_cmd.resampling_time_range = _K1_FIXED_TARGET_RESAMPLING_TIME_RANGE
+ goal_pose_cmd.resample_on_near_goal = False
+ goal_pose_cmd.resample_position_tolerance = _K1_TARGET_POSITION_TOLERANCE
+ goal_pose_cmd.resample_yaw_tolerance = _K1_TARGET_YAW_TOLERANCE
+
+ return cfg
diff --git a/src/mjlab_playground/tasks/target_location_amp/config/k1/rl_cfg.py b/src/mjlab_playground/tasks/target_location_amp/config/k1/rl_cfg.py
new file mode 100644
index 0000000..46a3eb0
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/config/k1/rl_cfg.py
@@ -0,0 +1,80 @@
+"""RL configuration for Booster K1 target-location AMP locomotion."""
+
+from __future__ import annotations
+
+from mjlab.rl import RslRlModelCfg
+
+from mjlab_playground.amp.rl.motion_loader import (
+ default_k1_locomotion_amp_motion_groups,
+)
+from mjlab_playground.amp.rl.runner_cfg import (
+ AmpOnPolicyRunnerCfg,
+ AmpPpoAlgorithmCfg,
+)
+
+_STABLE_ACTION_DISTRIBUTION = {
+ "class_name": "mjlab_playground.amp.rl.distributions:ClampedGaussianDistribution",
+ "init_std": 0.45,
+ "std_type": "log",
+ "min_std": 0.05,
+ "max_std": 1.0,
+}
+
+
+def booster_k1_target_location_amp_runner_cfg() -> AmpOnPolicyRunnerCfg:
+ """Create RL runner configuration for K1 target-location locomotion AMP."""
+ return AmpOnPolicyRunnerCfg(
+ actor=RslRlModelCfg(
+ hidden_dims=(512, 256, 128),
+ activation="elu",
+ obs_normalization=True,
+ distribution_cfg=dict(_STABLE_ACTION_DISTRIBUTION),
+ ),
+ critic=RslRlModelCfg(
+ hidden_dims=(512, 256, 128),
+ activation="elu",
+ obs_normalization=True,
+ ),
+ algorithm=AmpPpoAlgorithmCfg(
+ value_loss_coef=1.0,
+ use_clipped_value_loss=True,
+ clip_param=0.2,
+ entropy_coef=0.001,
+ num_learning_epochs=5,
+ num_mini_batches=4,
+ learning_rate=1.0e-3,
+ schedule="adaptive",
+ gamma=0.98,
+ lam=0.95,
+ desired_kl=0.01,
+ max_grad_norm=1.0,
+ amp_cfg={
+ "motion_groups": default_k1_locomotion_amp_motion_groups(),
+ "target_fps": 50.0,
+ "expected_dof": 22,
+ "reward_coef": 0.6,
+ "reward_scale": 1.0,
+ "task_reward_lerp": 0.45,
+ "loss_coef": 1.0,
+ "grad_penalty_coef": 10.0,
+ "discriminator_learning_rate": 3.0e-4,
+ "replay_buffer_size": 100_000,
+ "discriminator_hidden_dims": (512, 256),
+ "discriminator_activation": "relu",
+ "normalize_input": True,
+ "discriminator_loss_type": "least_squares",
+ "discriminator_reward_type": "hardware_lsgan",
+ "discriminator_expert_label": 0.9,
+ "discriminator_policy_label": 0.1,
+ "discriminator_reward_clip_max": 1.5,
+ },
+ symmetry_cfg=None,
+ ),
+ experiment_name="k1_target_location_amp",
+ run_name="k1_target_location_amp",
+ wandb_project="mjlab_playground",
+ save_interval=50,
+ num_steps_per_env=24,
+ max_iterations=10_000,
+ clip_actions=1.2,
+ )
diff --git a/src/mjlab_playground/tasks/target_location_amp/config/t1/__init__.py b/src/mjlab_playground/tasks/target_location_amp/config/t1/__init__.py
new file mode 100644
index 0000000..64e5f2c
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/config/t1/__init__.py
@@ -0,0 +1,42 @@
+"""Booster T1 target-location AMP task registration."""
+
+from mjlab.tasks.registry import register_mjlab_task
+
+from mjlab_playground.amp.rl import AmpOnPolicyRunner
+
+from .env_cfgs import (
+ booster_t1_target_location_amp_competition_collision_flat_env_cfg,
+ booster_t1_target_location_amp_competition_foot_flat_env_cfg,
+ booster_t1_target_location_amp_flat_env_cfg,
+)
+from .rl_cfg import (
+ booster_t1_target_location_amp_competition_collision_runner_cfg,
+ booster_t1_target_location_amp_competition_foot_runner_cfg,
+ booster_t1_target_location_amp_runner_cfg,
+)
+
+register_mjlab_task(
+ task_id="Mjlab-TargetLocationAmp-Flat-Booster-T1",
+ env_cfg=booster_t1_target_location_amp_flat_env_cfg(),
+ play_env_cfg=booster_t1_target_location_amp_flat_env_cfg(play=True),
+ rl_cfg=booster_t1_target_location_amp_runner_cfg(),
+ runner_cls=AmpOnPolicyRunner,
+)
+
+register_mjlab_task(
+ task_id="Mjlab-TargetLocationAmp-CompetitionFoot-Flat-Booster-T1",
+ env_cfg=booster_t1_target_location_amp_competition_foot_flat_env_cfg(),
+ play_env_cfg=booster_t1_target_location_amp_competition_foot_flat_env_cfg(play=True),
+ rl_cfg=booster_t1_target_location_amp_competition_foot_runner_cfg(),
+ runner_cls=AmpOnPolicyRunner,
+)
+
+register_mjlab_task(
+ task_id="Mjlab-TargetLocationAmp-CompetitionCollision-Flat-Booster-T1",
+ env_cfg=booster_t1_target_location_amp_competition_collision_flat_env_cfg(),
+ play_env_cfg=booster_t1_target_location_amp_competition_collision_flat_env_cfg(
+ play=True
+ ),
+ rl_cfg=booster_t1_target_location_amp_competition_collision_runner_cfg(),
+ runner_cls=AmpOnPolicyRunner,
+)
diff --git a/src/mjlab_playground/tasks/target_location_amp/config/t1/env_cfgs.py b/src/mjlab_playground/tasks/target_location_amp/config/t1/env_cfgs.py
new file mode 100644
index 0000000..802dfa7
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/config/t1/env_cfgs.py
@@ -0,0 +1,589 @@
+"""Booster T1 target-location AMP environment configuration."""
+
+from __future__ import annotations
+
+import math
+from copy import deepcopy
+
+from mjlab.envs import ManagerBasedRlEnvCfg
+from mjlab.envs import mdp as env_mdp
+from mjlab.envs.mdp import dr
+from mjlab.envs.mdp.actions import JointPositionActionCfg
+from mjlab.managers.curriculum_manager import CurriculumTermCfg
+from mjlab.managers.event_manager import EventTermCfg
+from mjlab.managers.reward_manager import RewardTermCfg
+from mjlab.managers.scene_entity_config import SceneEntityCfg
+
+from mjlab_playground.amp.mdp.actions import DelayedJointPositionActionCfg
+from mjlab_playground.asset_zoo.robots.booster_t1.t1_constants import (
+ T1_ACTION_SCALE,
+ get_t1_competition_collision_robot_cfg,
+ get_t1_competition_foot_robot_cfg,
+ get_t1_robot_cfg,
+)
+from mjlab_playground.tasks.target_location_amp import mdp as target_mdp
+from mjlab_playground.tasks.target_location_amp.target_location_amp_env_cfg import (
+ make_target_location_amp_env_cfg,
+)
+
+_T1_TARGET_POSITION_TOLERANCE = 0.22
+_T1_TARGET_YAW_TOLERANCE = 0.30
+_T1_NEAR_GOAL_DISTANCE = 0.85
+_T1_REWARD_GATE_MIN_ROOT_HEIGHT = 0.58
+_T1_REWARD_GATE_MIN_UP_Z = 0.80
+_T1_FALL_HEIGHT_THRESHOLD = 0.50
+_T1_FALL_UP_Z_THRESHOLD = 0.70
+_T1_EPISODE_LENGTH_S = 20.0
+_T1_TARGET_STABLE_HOLD_STEPS = 25
+_T1_TARGET_MAX_LIN_SPEED = 0.35
+_T1_TARGET_MAX_YAW_RATE = 0.60
+_T1_TARGET_MIN_UP_Z = 0.75
+_T1_RL_STEPS_PER_ITERATION = 24
+_T1_COMPETITION_ACTION_DELAY_STEPS = 1
+_T1_FINE_TUNE_ITERATION = 70_500
+_T1_FINE_TARGET_POSITION_TOLERANCE = 0.08
+_T1_FINE_TARGET_YAW_TOLERANCE = math.radians(6.0)
+_T1_FINE_NEAR_GOAL_DISTANCE = 0.45
+_T1_FINE_TARGET_STABLE_HOLD_STEPS = 25
+_T1_FINE_TARGET_MAX_LIN_SPEED = 0.25
+_T1_FINE_TARGET_MAX_YAW_RATE = 0.35
+_T1_FINE_TARGET_MIN_UP_Z = 0.80
+_T1_FINE_NEAR_GOAL_STABILITY_WEIGHT = 0.50
+_T1_FINE_GOAL_POSITION_BONUS_WEIGHT = 0.50
+_T1_FINE_GOAL_POSE_BONUS_WEIGHT = 1.00
+_T1_FINE_JOINT_TARGET_LIMITS_WEIGHT = -0.03
+_T1_FOOT_FRICTION_GEOMS = r"^(left|right)_foot[1-4]_collision$"
+_T1_COMPETITION_FOOT_FRICTION_GEOMS = r"^(left|right)_foot$"
+_T1_FINE_TARGET_COMMAND_BUCKETS = [
+ {
+ "radius_range": (0.0, 0.0),
+ "yaw_range": (0.0, 0.0),
+ "angle_range": (0.0, 0.0),
+ "probability": 0.1,
+ },
+ {
+ "radius_range": (0.0, 0.0),
+ "yaw_range": (-math.pi, math.pi),
+ "angle_range": (0.0, 0.0),
+ "probability": 0.1,
+ },
+ {
+ "radius_range": (0.0, 2.0),
+ "yaw_range": None,
+ "angle_range": None,
+ "probability": 0.3,
+ },
+ {
+ "radius_range": (5.0, 6.0),
+ "yaw_range": None,
+ "angle_range": None,
+ "probability": 0.4,
+ },
+ {
+ "radius_range": (2.0, 4.0),
+ "yaw_range": None,
+ "angle_range": None,
+ "probability": 0.1,
+ },
+]
+_T1_TARGET_LOCO_GOAL_CURRICULUM_STAGES = [
+ {
+ "step": 0,
+ "target_radius": (0.3, 0.8),
+ "target_yaw": (-0.2, 0.2),
+ "target_angle": (-0.4, 0.4),
+ "resampling_time_range": (4.0, 8.0),
+ "resample_on_near_goal": True,
+ "resample_on_goal_success": False,
+ "resample_position_tolerance": _T1_TARGET_POSITION_TOLERANCE,
+ "resample_yaw_tolerance": _T1_TARGET_YAW_TOLERANCE,
+ },
+ {
+ "step": 2_000 * _T1_RL_STEPS_PER_ITERATION,
+ "target_radius": (0.3, 1.5),
+ "target_yaw": (-0.5, 0.5),
+ "target_angle": (-1.0, 1.0),
+ "resampling_time_range": (4.0, 8.0),
+ "resample_on_near_goal": True,
+ "resample_on_goal_success": False,
+ "resample_position_tolerance": _T1_TARGET_POSITION_TOLERANCE,
+ "resample_yaw_tolerance": _T1_TARGET_YAW_TOLERANCE,
+ },
+ {
+ "step": 4_000 * _T1_RL_STEPS_PER_ITERATION,
+ "target_radius": (0.5, 3.0),
+ "target_yaw": (-1.0, 1.0),
+ "target_angle": (-math.pi, math.pi),
+ "resampling_time_range": (4.0, 8.0),
+ "resample_on_near_goal": True,
+ "resample_on_goal_success": False,
+ "resample_position_tolerance": _T1_TARGET_POSITION_TOLERANCE,
+ "resample_yaw_tolerance": _T1_TARGET_YAW_TOLERANCE,
+ },
+ {
+ "step": 7_000 * _T1_RL_STEPS_PER_ITERATION,
+ "target_radius": (0.5, 5.0),
+ "target_yaw": (-math.pi, math.pi),
+ "target_angle": (-math.pi, math.pi),
+ "resampling_time_range": (4.0, 8.0),
+ "resample_on_near_goal": True,
+ "resample_on_goal_success": False,
+ "resample_position_tolerance": _T1_TARGET_POSITION_TOLERANCE,
+ "resample_yaw_tolerance": _T1_TARGET_YAW_TOLERANCE,
+ },
+ {
+ "step": 24_000 * _T1_RL_STEPS_PER_ITERATION,
+ "target_radius": (1.0, 7.0),
+ "target_yaw": (-math.pi, math.pi),
+ "target_angle": (-math.pi, math.pi),
+ "resampling_time_range": (5.0, 10.0),
+ "resample_on_near_goal": True,
+ "resample_on_goal_success": False,
+ "resample_position_tolerance": _T1_TARGET_POSITION_TOLERANCE,
+ "resample_yaw_tolerance": _T1_TARGET_YAW_TOLERANCE,
+ },
+ {
+ "step": 27_000 * _T1_RL_STEPS_PER_ITERATION,
+ "target_radius": (1.0, 10.0),
+ "target_yaw": (-math.pi, math.pi),
+ "target_angle": (-math.pi, math.pi),
+ "resampling_time_range": (2.0, 12.0),
+ "resample_on_near_goal": True,
+ "resample_on_goal_success": False,
+ "resample_position_tolerance": _T1_TARGET_POSITION_TOLERANCE,
+ "resample_yaw_tolerance": _T1_TARGET_YAW_TOLERANCE,
+ },
+ {
+ "step": _T1_FINE_TUNE_ITERATION * _T1_RL_STEPS_PER_ITERATION,
+ "target_radius": (0.0, 6.0),
+ "target_command_buckets": _T1_FINE_TARGET_COMMAND_BUCKETS,
+ "target_yaw": (-math.pi, math.pi),
+ "target_angle": (-math.pi, math.pi),
+ "resampling_time_range": (1.0, 4.0),
+ "resample_on_near_goal": False,
+ "resample_on_goal_success": True,
+ "resample_position_tolerance": _T1_FINE_TARGET_POSITION_TOLERANCE,
+ "resample_yaw_tolerance": _T1_FINE_TARGET_YAW_TOLERANCE,
+ "goal_position_tolerance": _T1_FINE_TARGET_POSITION_TOLERANCE,
+ "goal_yaw_tolerance": _T1_FINE_TARGET_YAW_TOLERANCE,
+ "goal_hold_steps": _T1_FINE_TARGET_STABLE_HOLD_STEPS,
+ "goal_max_lin_speed": _T1_FINE_TARGET_MAX_LIN_SPEED,
+ "goal_max_yaw_rate": _T1_FINE_TARGET_MAX_YAW_RATE,
+ "goal_min_up_z": _T1_FINE_TARGET_MIN_UP_Z,
+ "near_goal_heading_distance": _T1_FINE_NEAR_GOAL_DISTANCE,
+ "near_goal_stability_weight": _T1_FINE_NEAR_GOAL_STABILITY_WEIGHT,
+ "near_goal_stability_distance": _T1_FINE_NEAR_GOAL_DISTANCE,
+ "near_goal_stability_lin_speed_std": 0.30,
+ "near_goal_stability_yaw_rate_std": 0.45,
+ "near_goal_stability_min_up_z": _T1_FINE_TARGET_MIN_UP_Z,
+ "goal_pose_bonus_weight": _T1_FINE_GOAL_POSE_BONUS_WEIGHT,
+ "joint_target_limits_weight": _T1_FINE_JOINT_TARGET_LIMITS_WEIGHT,
+ },
+]
+
+
+def _robot_asset_cfg() -> SceneEntityCfg:
+ return SceneEntityCfg("robot", joint_names=(".*",))
+
+
+def _apply_t1_joint_action_setup(
+ cfg: ManagerBasedRlEnvCfg,
+ *,
+ action_delay_steps: int = 0,
+) -> None:
+ action_cfg_type = (
+ DelayedJointPositionActionCfg if action_delay_steps > 0 else JointPositionActionCfg
+ )
+ action_cfg_kwargs = {}
+ if action_delay_steps > 0:
+ action_cfg_kwargs["delay_steps"] = action_delay_steps
+ cfg.actions["joint_pos"] = action_cfg_type(
+ entity_name="robot",
+ actuator_names=(".*",),
+ scale=T1_ACTION_SCALE,
+ clip=None,
+ use_default_offset=True,
+ **action_cfg_kwargs,
+ )
+
+
+def _configure_t1_domain_randomization(
+ cfg: ManagerBasedRlEnvCfg,
+ *,
+ foot_geom_names: str = _T1_FOOT_FRICTION_GEOMS,
+) -> None:
+ cfg.events["push_robot"] = EventTermCfg(
+ func=env_mdp.push_by_setting_velocity,
+ mode="interval",
+ interval_range_s=(1.0, 3.0),
+ params={
+ "velocity_range": {
+ "x": (-0.5, 0.5),
+ "y": (-0.5, 0.5),
+ "z": (-0.4, 0.4),
+ "roll": (-0.52, 0.52),
+ "pitch": (-0.52, 0.52),
+ "yaw": (-0.78, 0.78),
+ },
+ },
+ )
+ cfg.events["foot_friction"] = EventTermCfg(
+ mode="startup",
+ func=dr.geom_friction,
+ params={
+ "asset_cfg": SceneEntityCfg(
+ "robot",
+ geom_names=foot_geom_names,
+ ),
+ "operation": "abs",
+ "ranges": (0.3, 1.2),
+ "shared_random": True,
+ },
+ )
+ cfg.events["encoder_bias"] = EventTermCfg(
+ mode="startup",
+ func=dr.encoder_bias,
+ params={
+ "asset_cfg": SceneEntityCfg("robot"),
+ "bias_range": (-0.015, 0.015),
+ },
+ )
+ cfg.events["base_com"] = EventTermCfg(
+ mode="startup",
+ func=dr.body_com_offset,
+ params={
+ "asset_cfg": SceneEntityCfg("robot", body_names=("Trunk",)),
+ "operation": "add",
+ "ranges": {
+ 0: (-0.025, 0.025),
+ 1: (-0.025, 0.025),
+ 2: (-0.03, 0.03),
+ },
+ },
+ )
+
+
+def _configure_t1_goal_rewards(cfg: ManagerBasedRlEnvCfg) -> None:
+ cfg.rewards["near_goal_stability"].weight = 0.0
+ cfg.rewards["near_goal_stability"].params["near_distance"] = 0.75
+ cfg.rewards["near_goal_stability"].params["position_tolerance"] = (
+ _T1_FINE_TARGET_POSITION_TOLERANCE
+ )
+ cfg.rewards["near_goal_stability"].params["yaw_tolerance"] = (
+ _T1_FINE_TARGET_YAW_TOLERANCE
+ )
+ cfg.rewards["near_goal_stability"].params["lin_speed_std"] = 0.45
+ cfg.rewards["near_goal_stability"].params["yaw_rate_std"] = 0.75
+ cfg.rewards["near_goal_stability"].params["min_up_z"] = 0.75
+
+ cfg.rewards["position_progress"].params["position_tolerance"] = (
+ _T1_TARGET_POSITION_TOLERANCE
+ )
+ cfg.rewards["position_progress"].params["yaw_tolerance"] = _T1_TARGET_YAW_TOLERANCE
+ cfg.rewards["position_progress"].params["entity_name"] = "robot"
+ cfg.rewards["position_progress"].params["min_root_height"] = (
+ _T1_REWARD_GATE_MIN_ROOT_HEIGHT
+ )
+ cfg.rewards["position_progress"].params["min_up_z"] = _T1_REWARD_GATE_MIN_UP_Z
+ cfg.rewards["near_goal_heading_alignment"].weight = 0.70
+ cfg.rewards["near_goal_heading_alignment"].params["near_distance"] = (
+ _T1_NEAR_GOAL_DISTANCE
+ )
+ cfg.rewards["near_goal_heading_alignment"].params["entity_name"] = "robot"
+ cfg.rewards["near_goal_heading_alignment"].params["min_root_height"] = (
+ _T1_REWARD_GATE_MIN_ROOT_HEIGHT
+ )
+ cfg.rewards["near_goal_heading_alignment"].params["min_up_z"] = (
+ _T1_REWARD_GATE_MIN_UP_Z
+ )
+ cfg.rewards["yaw_progress"].params["position_tolerance"] = (
+ _T1_TARGET_POSITION_TOLERANCE
+ )
+ cfg.rewards["yaw_progress"].params["yaw_tolerance"] = _T1_TARGET_YAW_TOLERANCE
+ cfg.rewards["yaw_progress"].params["near_distance"] = _T1_NEAR_GOAL_DISTANCE
+ cfg.rewards["yaw_progress"].params["entity_name"] = "robot"
+ cfg.rewards["yaw_progress"].params["min_root_height"] = (
+ _T1_REWARD_GATE_MIN_ROOT_HEIGHT
+ )
+ cfg.rewards["yaw_progress"].params["min_up_z"] = _T1_REWARD_GATE_MIN_UP_Z
+ cfg.rewards["goal_position_bonus"].params["position_tolerance"] = (
+ _T1_TARGET_POSITION_TOLERANCE
+ )
+ cfg.rewards["goal_position_bonus"].weight = _T1_FINE_GOAL_POSITION_BONUS_WEIGHT
+ cfg.rewards["goal_position_bonus"].params["yaw_soft_tolerance"] = (
+ _T1_TARGET_YAW_TOLERANCE
+ )
+ cfg.rewards["goal_position_bonus"].params["entity_name"] = "robot"
+ cfg.rewards["goal_position_bonus"].params["min_root_height"] = (
+ _T1_REWARD_GATE_MIN_ROOT_HEIGHT
+ )
+ cfg.rewards["goal_position_bonus"].params["min_up_z"] = _T1_REWARD_GATE_MIN_UP_Z
+ cfg.rewards["goal_pose_bonus"].weight = 0.0
+ cfg.rewards["goal_pose_bonus"].params["position_tolerance"] = (
+ _T1_TARGET_POSITION_TOLERANCE
+ )
+ cfg.rewards["goal_pose_bonus"].params["yaw_tolerance"] = _T1_TARGET_YAW_TOLERANCE
+ cfg.rewards["goal_pose_bonus"].params["hold_steps"] = _T1_TARGET_STABLE_HOLD_STEPS
+ cfg.rewards["goal_pose_bonus"].params["entity_name"] = "robot"
+ cfg.rewards["goal_pose_bonus"].params["max_lin_speed"] = _T1_TARGET_MAX_LIN_SPEED
+ cfg.rewards["goal_pose_bonus"].params["max_yaw_rate"] = _T1_TARGET_MAX_YAW_RATE
+ cfg.rewards["goal_pose_bonus"].params["min_up_z"] = _T1_TARGET_MIN_UP_Z
+ cfg.rewards["goal_time_efficiency"].params["position_tolerance"] = (
+ _T1_TARGET_POSITION_TOLERANCE
+ )
+ cfg.rewards["goal_time_efficiency"].params["yaw_tolerance"] = _T1_TARGET_YAW_TOLERANCE
+ cfg.rewards["goal_time_efficiency"].params["hold_steps"] = (
+ _T1_TARGET_STABLE_HOLD_STEPS
+ )
+ cfg.rewards["goal_time_efficiency"].params["entity_name"] = "robot"
+ cfg.rewards["goal_time_efficiency"].params["max_lin_speed"] = _T1_TARGET_MAX_LIN_SPEED
+ cfg.rewards["goal_time_efficiency"].params["max_yaw_rate"] = _T1_TARGET_MAX_YAW_RATE
+ cfg.rewards["goal_time_efficiency"].params["min_up_z"] = _T1_TARGET_MIN_UP_Z
+ cfg.rewards["fall_penalty"].weight = -0.2
+ cfg.rewards["fall_penalty"].params["height_threshold"] = _T1_FALL_HEIGHT_THRESHOLD
+ cfg.rewards["fall_penalty"].params["up_z_threshold"] = _T1_FALL_UP_Z_THRESHOLD
+
+
+def _apply_t1_goal_stage_to_reward_cfg(cfg: ManagerBasedRlEnvCfg, stage: dict) -> None:
+ if stage.get("goal_position_tolerance") is not None:
+ position_tolerance = stage["goal_position_tolerance"]
+ cfg.rewards["position_progress"].params["position_tolerance"] = position_tolerance
+ cfg.rewards["yaw_progress"].params["position_tolerance"] = position_tolerance
+ cfg.rewards["goal_position_bonus"].params["position_tolerance"] = position_tolerance
+ cfg.rewards["goal_pose_bonus"].params["position_tolerance"] = position_tolerance
+ cfg.rewards["goal_time_efficiency"].params["position_tolerance"] = (
+ position_tolerance
+ )
+
+ if stage.get("goal_yaw_tolerance") is not None:
+ yaw_tolerance = stage["goal_yaw_tolerance"]
+ cfg.rewards["position_progress"].params["yaw_tolerance"] = yaw_tolerance
+ cfg.rewards["yaw_progress"].params["yaw_tolerance"] = yaw_tolerance
+ cfg.rewards["goal_position_bonus"].params["yaw_soft_tolerance"] = yaw_tolerance
+ cfg.rewards["goal_pose_bonus"].params["yaw_tolerance"] = yaw_tolerance
+ cfg.rewards["goal_time_efficiency"].params["yaw_tolerance"] = yaw_tolerance
+
+ if stage.get("goal_hold_steps") is not None:
+ hold_steps = stage["goal_hold_steps"]
+ cfg.rewards["goal_pose_bonus"].params["hold_steps"] = hold_steps
+ cfg.rewards["goal_time_efficiency"].params["hold_steps"] = hold_steps
+
+ if stage.get("goal_max_lin_speed") is not None:
+ max_lin_speed = stage["goal_max_lin_speed"]
+ cfg.rewards["goal_pose_bonus"].params["max_lin_speed"] = max_lin_speed
+ cfg.rewards["goal_time_efficiency"].params["max_lin_speed"] = max_lin_speed
+
+ if stage.get("goal_max_yaw_rate") is not None:
+ max_yaw_rate = stage["goal_max_yaw_rate"]
+ cfg.rewards["goal_pose_bonus"].params["max_yaw_rate"] = max_yaw_rate
+ cfg.rewards["goal_time_efficiency"].params["max_yaw_rate"] = max_yaw_rate
+
+ if stage.get("goal_min_up_z") is not None:
+ min_up_z = stage["goal_min_up_z"]
+ cfg.rewards["goal_pose_bonus"].params["min_up_z"] = min_up_z
+ cfg.rewards["goal_time_efficiency"].params["min_up_z"] = min_up_z
+
+ if stage.get("near_goal_heading_distance") is not None:
+ near_distance = stage["near_goal_heading_distance"]
+ cfg.rewards["near_goal_heading_alignment"].params["near_distance"] = near_distance
+ cfg.rewards["yaw_progress"].params["near_distance"] = near_distance
+
+ if stage.get("near_goal_stability_weight") is not None:
+ cfg.rewards["near_goal_stability"].weight = stage["near_goal_stability_weight"]
+ if stage.get("near_goal_stability_distance") is not None:
+ cfg.rewards["near_goal_stability"].params["near_distance"] = stage[
+ "near_goal_stability_distance"
+ ]
+ if stage.get("near_goal_stability_lin_speed_std") is not None:
+ cfg.rewards["near_goal_stability"].params["lin_speed_std"] = stage[
+ "near_goal_stability_lin_speed_std"
+ ]
+ if stage.get("near_goal_stability_yaw_rate_std") is not None:
+ cfg.rewards["near_goal_stability"].params["yaw_rate_std"] = stage[
+ "near_goal_stability_yaw_rate_std"
+ ]
+ if stage.get("near_goal_stability_min_up_z") is not None:
+ cfg.rewards["near_goal_stability"].params["min_up_z"] = stage[
+ "near_goal_stability_min_up_z"
+ ]
+
+ if stage.get("goal_pose_bonus_weight") is not None:
+ cfg.rewards["goal_pose_bonus"].weight = stage["goal_pose_bonus_weight"]
+
+ if stage.get("joint_target_limits_weight") is not None:
+ cfg.rewards["joint_target_limits"].weight = stage["joint_target_limits_weight"]
+
+
+def _apply_t1_goal_stage_to_command_cfg(
+ goal_pose_cmd: target_mdp.GoalPoseCommandCfg, stage: dict
+) -> None:
+ goal_pose_cmd.ranges.target_radius = stage["target_radius"]
+ goal_pose_cmd.ranges.target_yaw = stage["target_yaw"]
+ goal_pose_cmd.ranges.target_angle = stage["target_angle"]
+ command_buckets = stage.get("target_command_buckets")
+ goal_pose_cmd.ranges.target_command_buckets = (
+ tuple(
+ target_mdp.GoalPoseCommandCfg.CommandBucket(
+ radius_range=tuple(bucket["radius_range"]),
+ probability=float(bucket["probability"]),
+ yaw_range=(
+ tuple(bucket["yaw_range"]) if bucket["yaw_range"] is not None else None
+ ),
+ angle_range=(
+ tuple(bucket["angle_range"]) if bucket["angle_range"] is not None else None
+ ),
+ )
+ for bucket in command_buckets
+ )
+ if command_buckets is not None
+ else None
+ )
+ radius_buckets = stage.get("target_radius_buckets")
+ goal_pose_cmd.ranges.target_radius_buckets = (
+ tuple(
+ target_mdp.GoalPoseCommandCfg.RadiusBucket(
+ radius_range=tuple(bucket["radius_range"]),
+ probability=float(bucket["probability"]),
+ )
+ for bucket in radius_buckets
+ )
+ if radius_buckets is not None
+ else None
+ )
+ goal_pose_cmd.resampling_time_range = stage["resampling_time_range"]
+ goal_pose_cmd.resample_on_near_goal = stage["resample_on_near_goal"]
+ goal_pose_cmd.resample_on_goal_success = stage["resample_on_goal_success"]
+ goal_pose_cmd.resample_position_tolerance = stage["resample_position_tolerance"]
+ goal_pose_cmd.resample_yaw_tolerance = stage["resample_yaw_tolerance"]
+
+
+def _configure_t1_goal_pose_command(cfg: ManagerBasedRlEnvCfg) -> None:
+ goal_pose_cmd = cfg.commands["goal_pose"]
+ assert isinstance(goal_pose_cmd, target_mdp.GoalPoseCommandCfg)
+ stage0 = _T1_TARGET_LOCO_GOAL_CURRICULUM_STAGES[0]
+ _apply_t1_goal_stage_to_command_cfg(goal_pose_cmd, stage0)
+
+
+def _configure_t1_competition_server_physics(cfg: ManagerBasedRlEnvCfg) -> None:
+ cfg.sim.mujoco.integrator = "euler"
+ cfg.sim.mujoco.cone = "pyramidal"
+ cfg.sim.mujoco.impratio = 1.0
+ cfg.sim.mujoco.iterations = 100
+ cfg.sim.mujoco.ls_iterations = 50
+ cfg.sim.mujoco.ccd_iterations = 35
+
+
+def _t1_play_stage() -> dict:
+ return _T1_TARGET_LOCO_GOAL_CURRICULUM_STAGES[-1]
+
+
+def booster_t1_target_location_amp_flat_env_cfg(
+ play: bool = False,
+ competition_foot: bool = False,
+ competition_collision: bool = False,
+) -> ManagerBasedRlEnvCfg:
+ """Create the T1 target-location locomotion AMP task."""
+ if competition_collision and competition_foot:
+ raise ValueError(
+ "competition_collision and competition_foot are mutually exclusive."
+ )
+
+ cfg = make_target_location_amp_env_cfg(play=play)
+ robot_cfg = get_t1_robot_cfg()
+ if competition_foot:
+ robot_cfg = get_t1_competition_foot_robot_cfg()
+ if competition_collision:
+ robot_cfg = get_t1_competition_collision_robot_cfg()
+ if competition_foot or competition_collision:
+ _configure_t1_competition_server_physics(cfg)
+ cfg.scene.entities = {
+ "robot": deepcopy(robot_cfg),
+ }
+ cfg.scene.num_envs = 1 if play else 1024
+ cfg.episode_length_s = _T1_EPISODE_LENGTH_S
+ cfg.viewer.body_name = "Trunk"
+ _apply_t1_joint_action_setup(
+ cfg,
+ action_delay_steps=(
+ _T1_COMPETITION_ACTION_DELAY_STEPS if competition_collision else 0
+ ),
+ )
+ _configure_t1_goal_rewards(cfg)
+ _configure_t1_goal_pose_command(cfg)
+ cfg.events["reset_robot_joints"].params["asset_cfg"] = _robot_asset_cfg()
+ if not play:
+ _configure_t1_domain_randomization(
+ cfg,
+ foot_geom_names=(
+ _T1_COMPETITION_FOOT_FRICTION_GEOMS
+ if competition_foot or competition_collision
+ else _T1_FOOT_FRICTION_GEOMS
+ ),
+ )
+ cfg.observations["critic"].terms["amp_robot"].params["robot_cfg"] = _robot_asset_cfg()
+ cfg.terminations["fell_over"].params["height_threshold"] = _T1_FALL_HEIGHT_THRESHOLD
+ cfg.terminations["fell_over"].params["up_z_threshold"] = _T1_FALL_UP_Z_THRESHOLD
+
+ cfg.rewards["action_rate_l2"] = RewardTermCfg(
+ func=target_mdp.action_rate_l2,
+ weight=-0.01,
+ )
+ cfg.rewards["joint_pos_limits"] = RewardTermCfg(
+ func=target_mdp.joint_pos_limits,
+ weight=-0.2,
+ params={"asset_cfg": _robot_asset_cfg()},
+ )
+ cfg.rewards["joint_target_limits"] = RewardTermCfg(
+ func=target_mdp.joint_target_pos_limits,
+ weight=0.0,
+ params={
+ "asset_cfg": _robot_asset_cfg(),
+ "action_name": "joint_pos",
+ },
+ )
+
+ if not play:
+ cfg.curriculum["goal_pose"] = CurriculumTermCfg(
+ func=target_mdp.goal_pose_ranges,
+ params={
+ "command_name": "goal_pose",
+ "stages": _T1_TARGET_LOCO_GOAL_CURRICULUM_STAGES,
+ },
+ )
+
+ if play:
+ cfg.observations["actor"].enable_corruption = False
+ goal_pose_cmd = cfg.commands["goal_pose"]
+ assert isinstance(goal_pose_cmd, target_mdp.GoalPoseCommandCfg)
+ play_stage = _t1_play_stage()
+ _apply_t1_goal_stage_to_command_cfg(goal_pose_cmd, play_stage)
+ _apply_t1_goal_stage_to_reward_cfg(cfg, play_stage)
+ cfg.curriculum["goal_pose"] = CurriculumTermCfg(
+ func=target_mdp.goal_pose_ranges,
+ params={
+ "command_name": "goal_pose",
+ "stages": _T1_TARGET_LOCO_GOAL_CURRICULUM_STAGES,
+ },
+ )
+
+ return cfg
+
+
+def booster_t1_target_location_amp_competition_foot_flat_env_cfg(
+ play: bool = False,
+) -> ManagerBasedRlEnvCfg:
+ """Create the T1 target-location AMP task with competition-style foot contacts."""
+ return booster_t1_target_location_amp_flat_env_cfg(
+ play=play,
+ competition_foot=True,
+ )
+
+
+def booster_t1_target_location_amp_competition_collision_flat_env_cfg(
+ play: bool = False,
+) -> ManagerBasedRlEnvCfg:
+ """Create the T1 target-location AMP task with competition-style collision contacts."""
+ return booster_t1_target_location_amp_flat_env_cfg(
+ play=play,
+ competition_collision=True,
+ )
diff --git a/src/mjlab_playground/tasks/target_location_amp/config/t1/rl_cfg.py b/src/mjlab_playground/tasks/target_location_amp/config/t1/rl_cfg.py
new file mode 100644
index 0000000..1efa7be
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/config/t1/rl_cfg.py
@@ -0,0 +1,105 @@
+"""RL configuration for Booster T1 target-location AMP locomotion."""
+
+from __future__ import annotations
+
+from mjlab.rl import RslRlModelCfg
+
+from mjlab_playground.amp.rl.motion_loader import (
+ default_t1_locomotion_amp_motion_groups,
+)
+from mjlab_playground.amp.rl.runner_cfg import (
+ AmpOnPolicyRunnerCfg,
+ AmpPpoAlgorithmCfg,
+)
+from mjlab_playground.asset_zoo.robots.booster_t1.t1_constants import T1_JOINT_NAMES
+
+_T1_ACTION_DISTRIBUTION = {
+ "class_name": "GaussianDistribution",
+ "init_std": 1.0,
+ "std_type": "log",
+}
+
+
+def booster_t1_target_location_amp_runner_cfg() -> AmpOnPolicyRunnerCfg:
+ """Create RL runner configuration for T1 target-location locomotion AMP."""
+ return AmpOnPolicyRunnerCfg(
+ actor=RslRlModelCfg(
+ hidden_dims=(512, 256, 128),
+ activation="elu",
+ obs_normalization=True,
+ distribution_cfg=dict(_T1_ACTION_DISTRIBUTION),
+ ),
+ critic=RslRlModelCfg(
+ hidden_dims=(512, 256, 128),
+ activation="elu",
+ obs_normalization=True,
+ ),
+ algorithm=AmpPpoAlgorithmCfg(
+ value_loss_coef=1.0,
+ use_clipped_value_loss=True,
+ clip_param=0.2,
+ entropy_coef=0.0005,
+ num_learning_epochs=5,
+ num_mini_batches=4,
+ learning_rate=1.0e-3,
+ schedule="adaptive",
+ gamma=0.98,
+ lam=0.95,
+ desired_kl=0.01,
+ max_grad_norm=1.0,
+ amp_cfg={
+ "motion_groups": default_t1_locomotion_amp_motion_groups(),
+ "target_fps": 50.0,
+ "expected_dof": 23,
+ "joint_names": T1_JOINT_NAMES,
+ "reward_coef": 0.6,
+ "reward_scale": 1.0,
+ "task_reward_lerp": 0.5,
+ "style_reward_gate": {
+ "entity_name": "robot",
+ "min_root_height": 0.58,
+ "min_up_z": 0.80,
+ "zero_on_done": True,
+ },
+ "loss_coef": 1.0,
+ "grad_penalty_coef": 10.0,
+ "discriminator_learning_rate": 3.0e-4,
+ "replay_buffer_size": 100_000,
+ "discriminator_hidden_dims": (512, 256),
+ "discriminator_activation": "relu",
+ "normalize_input": True,
+ "discriminator_loss_type": "least_squares",
+ "discriminator_reward_type": "hardware_lsgan",
+ "discriminator_expert_label": 0.9,
+ "discriminator_policy_label": 0.1,
+ "discriminator_reward_clip_max": 1.5,
+ },
+ symmetry_cfg=None,
+ ),
+ experiment_name="t1_target_location_amp",
+ run_name="t1_target_location_amp",
+ wandb_project="mjlab_playground",
+ num_steps_per_env=24,
+ max_iterations=10_000,
+ clip_actions=None,
+ )
+
+
+def booster_t1_target_location_amp_competition_foot_runner_cfg() -> (
+ AmpOnPolicyRunnerCfg
+):
+ """Create RL runner config for T1 target-location AMP with box foot contacts."""
+ cfg = booster_t1_target_location_amp_runner_cfg()
+ cfg.experiment_name = "t1_target_location_amp_competition_foot"
+ cfg.run_name = "t1_target_location_amp_competition_foot"
+ return cfg
+
+
+def booster_t1_target_location_amp_competition_collision_runner_cfg() -> (
+ AmpOnPolicyRunnerCfg
+):
+ """Create RL runner config for T1 target-location AMP with competition collisions."""
+ cfg = booster_t1_target_location_amp_runner_cfg()
+ cfg.experiment_name = "t1_target_location_amp_competition_collision"
+ cfg.run_name = "t1_target_location_amp_competition_collision"
+ return cfg
diff --git a/src/mjlab_playground/tasks/target_location_amp/mdp/__init__.py b/src/mjlab_playground/tasks/target_location_amp/mdp/__init__.py
new file mode 100644
index 0000000..be79dbd
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/mdp/__init__.py
@@ -0,0 +1,63 @@
+"""MDP helpers specific to target-location AMP locomotion."""
+
+from .commands import GoalPoseCommand, GoalPoseCommandCfg
+from .curriculums import GoalStage, goal_pose_ranges
+from .observations import (
+ final_yaw_error_features,
+ goal_distance,
+ goal_local_position,
+ heading_to_target_features,
+)
+from .rewards import (
+ action_rate_l2,
+ alive_reward,
+ body_ang_vel_xy_l2,
+ distance_error_penalty,
+ goal_pose_terminal_bonus,
+ goal_position_bonus,
+ goal_time_efficiency_bonus,
+ joint_pos_limits,
+ joint_target_pos_limits,
+ near_goal_heading_alignment_reward,
+ near_goal_stability_reward,
+ position_progress_reward,
+ root_height_reward,
+ upright_orientation_reward,
+ yaw_progress_reward,
+)
+from .state import (
+ reset_goal_pose_state,
+ update_goal_pose_state,
+ update_goal_pose_success_hold_state,
+)
+from .terminations import goal_pose_reached
+
+__all__ = [
+ "GoalPoseCommand",
+ "GoalPoseCommandCfg",
+ "GoalStage",
+ "action_rate_l2",
+ "alive_reward",
+ "body_ang_vel_xy_l2",
+ "distance_error_penalty",
+ "final_yaw_error_features",
+ "goal_distance",
+ "goal_local_position",
+ "goal_pose_reached",
+ "goal_pose_ranges",
+ "goal_pose_terminal_bonus",
+ "goal_position_bonus",
+ "goal_time_efficiency_bonus",
+ "heading_to_target_features",
+ "joint_pos_limits",
+ "joint_target_pos_limits",
+ "near_goal_stability_reward",
+ "near_goal_heading_alignment_reward",
+ "position_progress_reward",
+ "reset_goal_pose_state",
+ "root_height_reward",
+ "upright_orientation_reward",
+ "update_goal_pose_state",
+ "update_goal_pose_success_hold_state",
+ "yaw_progress_reward",
+]
diff --git a/src/mjlab_playground/tasks/target_location_amp/mdp/commands.py b/src/mjlab_playground/tasks/target_location_amp/mdp/commands.py
new file mode 100644
index 0000000..f088351
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/mdp/commands.py
@@ -0,0 +1,394 @@
+"""Goal-pose command local to the target-location AMP task."""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass, field
+
+import numpy as np
+import torch
+from mjlab.entity import Entity
+from mjlab.managers import CommandTerm, CommandTermCfg
+from mjlab.utils.lab_api.math import wrap_to_pi
+
+from .rewards import goal_stability_mask
+from .state import reset_goal_pose_state, update_goal_pose_success_hold_state
+
+
+class GoalPoseCommand(CommandTerm):
+ cfg: "GoalPoseCommandCfg"
+
+ def __init__(self, cfg: "GoalPoseCommandCfg", env):
+ super().__init__(cfg, env)
+ self.robot: Entity = env.scene[cfg.entity_name]
+ self.target_pos_w = torch.zeros(self.num_envs, 2, device=self.device)
+ self.target_yaw_w = torch.zeros(self.num_envs, device=self.device)
+ self.initial_distance_to_goal = torch.zeros(self.num_envs, device=self.device)
+ self._target_offset_b = torch.zeros(self.num_envs, 2, device=self.device)
+ self._target_yaw_offset = torch.zeros(self.num_envs, device=self.device)
+ self._target_needs_anchor = torch.ones(
+ self.num_envs, dtype=torch.bool, device=self.device
+ )
+ self.local_pos_error = torch.zeros(self.num_envs, 2, device=self.device)
+ self.distance_to_goal = torch.zeros(self.num_envs, device=self.device)
+ self.heading_to_target = torch.zeros(self.num_envs, device=self.device)
+ self.final_heading_error = torch.zeros(self.num_envs, device=self.device)
+ self._resample_on_next_step = torch.zeros(
+ self.num_envs, dtype=torch.bool, device=self.device
+ )
+ self.metrics["goal_distance"] = torch.zeros(self.num_envs, device=self.device)
+ self.metrics["goal_final_yaw_abs"] = torch.zeros(self.num_envs, device=self.device)
+
+ @property
+ def command(self) -> torch.Tensor:
+ return torch.cat(
+ (
+ self.local_pos_error,
+ self.final_heading_error.unsqueeze(-1),
+ ),
+ dim=-1,
+ )
+
+ def _update_metrics(self) -> None:
+ self.metrics["goal_distance"] = self.distance_to_goal
+ self.metrics["goal_final_yaw_abs"] = torch.abs(self.final_heading_error)
+
+ def _sample_bucket_ids(
+ self,
+ probabilities: list[float],
+ num_samples: int,
+ error_message: str,
+ ) -> torch.Tensor:
+ probs = torch.tensor(
+ [max(float(probability), 0.0) for probability in probabilities],
+ dtype=torch.float32,
+ device=self.device,
+ )
+ total_probability = float(probs.sum().item())
+ if total_probability <= 0.0:
+ raise ValueError(error_message)
+ probs = probs / total_probability
+ return torch.multinomial(probs, num_samples, replacement=True)
+
+ def _sample_goal_components(
+ self, env_ids: torch.Tensor
+ ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ command_buckets = self.cfg.ranges.target_command_buckets
+ if command_buckets:
+ bucket_ids = self._sample_bucket_ids(
+ [bucket.probability for bucket in command_buckets],
+ len(env_ids),
+ "target_command_buckets must contain positive probability mass.",
+ )
+ radius_min = torch.tensor(
+ [float(bucket.radius_range[0]) for bucket in command_buckets],
+ dtype=torch.float32,
+ device=self.device,
+ )[bucket_ids]
+ radius_max = torch.tensor(
+ [float(bucket.radius_range[1]) for bucket in command_buckets],
+ dtype=torch.float32,
+ device=self.device,
+ )[bucket_ids]
+ angle_min = torch.tensor(
+ [
+ float(
+ bucket.angle_range[0]
+ if bucket.angle_range is not None
+ else self.cfg.ranges.target_angle[0]
+ )
+ for bucket in command_buckets
+ ],
+ dtype=torch.float32,
+ device=self.device,
+ )[bucket_ids]
+ angle_max = torch.tensor(
+ [
+ float(
+ bucket.angle_range[1]
+ if bucket.angle_range is not None
+ else self.cfg.ranges.target_angle[1]
+ )
+ for bucket in command_buckets
+ ],
+ dtype=torch.float32,
+ device=self.device,
+ )[bucket_ids]
+ yaw_min = torch.tensor(
+ [
+ float(
+ bucket.yaw_range[0]
+ if bucket.yaw_range is not None
+ else self.cfg.ranges.target_yaw[0]
+ )
+ for bucket in command_buckets
+ ],
+ dtype=torch.float32,
+ device=self.device,
+ )[bucket_ids]
+ yaw_max = torch.tensor(
+ [
+ float(
+ bucket.yaw_range[1]
+ if bucket.yaw_range is not None
+ else self.cfg.ranges.target_yaw[1]
+ )
+ for bucket in command_buckets
+ ],
+ dtype=torch.float32,
+ device=self.device,
+ )[bucket_ids]
+ target_radius = radius_min + torch.rand(len(env_ids), device=self.device) * (
+ radius_max - radius_min
+ )
+ target_angle = angle_min + torch.rand(len(env_ids), device=self.device) * (
+ angle_max - angle_min
+ )
+ yaw_offset = yaw_min + torch.rand(len(env_ids), device=self.device) * (
+ yaw_max - yaw_min
+ )
+ return target_radius, target_angle, yaw_offset
+
+ target_radius = self._sample_target_radius(env_ids)
+ target_angle = torch.empty(len(env_ids), device=self.device).uniform_(
+ *self.cfg.ranges.target_angle
+ )
+ yaw_offset = torch.empty(len(env_ids), device=self.device).uniform_(
+ *self.cfg.ranges.target_yaw
+ )
+ return target_radius, target_angle, yaw_offset
+
+ def _sample_target_radius(self, env_ids: torch.Tensor) -> torch.Tensor:
+ buckets = self.cfg.ranges.target_radius_buckets
+ if not buckets:
+ return torch.empty(len(env_ids), device=self.device).uniform_(
+ *self.cfg.ranges.target_radius
+ )
+
+ bucket_ids = self._sample_bucket_ids(
+ [bucket.probability for bucket in buckets],
+ len(env_ids),
+ "target_radius_buckets must contain positive probability mass.",
+ )
+ radius_min = torch.tensor(
+ [float(bucket.radius_range[0]) for bucket in buckets],
+ dtype=torch.float32,
+ device=self.device,
+ )[bucket_ids]
+ radius_max = torch.tensor(
+ [float(bucket.radius_range[1]) for bucket in buckets],
+ dtype=torch.float32,
+ device=self.device,
+ )[bucket_ids]
+ return radius_min + torch.rand(len(env_ids), device=self.device) * (
+ radius_max - radius_min
+ )
+
+ def _resample_command(self, env_ids: torch.Tensor) -> None:
+ if len(env_ids) == 0:
+ return
+
+ target_radius, target_angle, yaw_offset = self._sample_goal_components(env_ids)
+ target_x = target_radius * torch.cos(target_angle)
+ target_y = target_radius * torch.sin(target_angle)
+ self.initial_distance_to_goal[env_ids] = target_radius
+ self._target_offset_b[env_ids, 0] = target_x
+ self._target_offset_b[env_ids, 1] = target_y
+ self._target_yaw_offset[env_ids] = yaw_offset
+ self._target_needs_anchor[env_ids] = True
+ self._resample_on_next_step[env_ids] = False
+ reset_goal_pose_state(self._env, env_ids)
+
+ def _anchor_pending_targets(self) -> None:
+ heading = self.robot.data.heading_w
+ root_pos_w = self.robot.data.root_link_pos_w[:, :2]
+ if torch.any(self._target_needs_anchor):
+ env_ids = self._target_needs_anchor.nonzero(as_tuple=False).flatten()
+ cos_h = torch.cos(heading[env_ids])
+ sin_h = torch.sin(heading[env_ids])
+ target_offset_b = self._target_offset_b[env_ids]
+ delta_w = torch.stack(
+ (
+ cos_h * target_offset_b[:, 0] - sin_h * target_offset_b[:, 1],
+ sin_h * target_offset_b[:, 0] + cos_h * target_offset_b[:, 1],
+ ),
+ dim=-1,
+ )
+ self.target_pos_w[env_ids] = root_pos_w[env_ids] + delta_w
+ self.target_yaw_w[env_ids] = wrap_to_pi(
+ heading[env_ids] + self._target_yaw_offset[env_ids]
+ )
+ self._target_needs_anchor[env_ids] = False
+
+ def _refresh_local_errors(self) -> None:
+ root_pos_w = self.robot.data.root_link_pos_w[:, :2]
+ heading = self.robot.data.heading_w
+ delta_w = self.target_pos_w - root_pos_w
+ cos_h = torch.cos(heading)
+ sin_h = torch.sin(heading)
+ self.local_pos_error[:, 0] = cos_h * delta_w[:, 0] + sin_h * delta_w[:, 1]
+ self.local_pos_error[:, 1] = -sin_h * delta_w[:, 0] + cos_h * delta_w[:, 1]
+ self.distance_to_goal = torch.norm(self.local_pos_error, dim=-1)
+ self.heading_to_target = torch.atan2(
+ self.local_pos_error[:, 1], self.local_pos_error[:, 0]
+ )
+ self.final_heading_error = wrap_to_pi(self.target_yaw_w - heading)
+
+ def _queue_near_goal_resample(self) -> None:
+ if not self.cfg.resample_on_near_goal:
+ return
+ reached_position = self.distance_to_goal <= self.cfg.resample_position_tolerance
+ reached_yaw = torch.abs(self.final_heading_error) <= self.cfg.resample_yaw_tolerance
+ self._resample_on_next_step |= reached_position & reached_yaw
+
+ def _queue_goal_success_resample(self) -> None:
+ if not self.cfg.resample_on_goal_success:
+ return
+
+ reward_manager = getattr(self._env, "reward_manager", None)
+ if reward_manager is None:
+ return
+ try:
+ goal_pose_bonus_cfg = reward_manager.get_term_cfg("goal_pose_bonus")
+ except (KeyError, ValueError, AttributeError):
+ return
+ params = getattr(goal_pose_bonus_cfg, "params", None)
+ if not isinstance(params, dict):
+ return
+
+ position_tolerance = float(
+ params.get("position_tolerance", self.cfg.resample_position_tolerance)
+ )
+ yaw_tolerance = float(params.get("yaw_tolerance", self.cfg.resample_yaw_tolerance))
+ hold_steps = int(params.get("hold_steps", 1))
+ entity_name = str(params.get("entity_name", self.cfg.entity_name))
+ max_lin_speed = params.get("max_lin_speed")
+ max_yaw_rate = params.get("max_yaw_rate")
+ min_up_z = params.get("min_up_z")
+
+ reached_now = (self.distance_to_goal <= position_tolerance) & (
+ torch.abs(self.final_heading_error) <= yaw_tolerance
+ )
+ if max_lin_speed is not None or max_yaw_rate is not None or min_up_z is not None:
+ stable, _ = goal_stability_mask(
+ self._env,
+ entity_name=entity_name,
+ max_lin_speed=(float(max_lin_speed) if max_lin_speed is not None else None),
+ max_yaw_rate=(float(max_yaw_rate) if max_yaw_rate is not None else None),
+ min_up_z=float(min_up_z) if min_up_z is not None else None,
+ )
+ reached_now = reached_now & stable
+ hold = update_goal_pose_success_hold_state(
+ self._env,
+ success_now=reached_now,
+ )
+ reached = hold >= hold_steps
+ self._resample_on_next_step |= reached
+
+ def _update_command(self) -> None:
+ if torch.any(self._resample_on_next_step):
+ env_ids = self._resample_on_next_step.nonzero(as_tuple=False).flatten()
+ self._resample(env_ids)
+ self._anchor_pending_targets()
+ self._refresh_local_errors()
+ self._queue_near_goal_resample()
+ self._queue_goal_success_resample()
+
+ def _debug_vis_impl(self, visualizer) -> None:
+ env_indices = visualizer.get_env_indices(self.num_envs)
+ if not env_indices:
+ return
+
+ root_pos_ws = self.robot.data.root_link_pos_w.cpu().numpy()
+ target_pos_ws = self.target_pos_w.cpu().numpy()
+ target_yaws = self.target_yaw_w.cpu().numpy()
+
+ z_offset = self.cfg.viz.z_offset
+ sphere_radius = self.cfg.viz.sphere_radius
+ heading_scale = self.cfg.viz.heading_scale
+ link_radius = self.cfg.viz.link_radius
+
+ for env_idx in env_indices:
+ root_pos_w = root_pos_ws[env_idx]
+ target_pos_w = target_pos_ws[env_idx]
+
+ if np.linalg.norm(root_pos_w) < 1.0e-6:
+ continue
+
+ target_point = np.array(
+ [target_pos_w[0], target_pos_w[1], z_offset], dtype=np.float32
+ )
+ robot_point = np.array([root_pos_w[0], root_pos_w[1], z_offset], dtype=np.float32)
+ target_yaw = float(target_yaws[env_idx])
+
+ visualizer.add_sphere(
+ target_point,
+ radius=sphere_radius,
+ color=(1.0, 0.55, 0.1, 0.9),
+ label=f"goal_target_{env_idx}",
+ )
+ visualizer.add_cylinder(
+ robot_point,
+ target_point,
+ radius=link_radius,
+ color=(1.0, 0.7, 0.2, 0.35),
+ label=f"goal_link_{env_idx}",
+ )
+
+ heading_end = np.array(
+ [
+ target_pos_w[0] + heading_scale * np.cos(target_yaw),
+ target_pos_w[1] + heading_scale * np.sin(target_yaw),
+ z_offset,
+ ],
+ dtype=np.float32,
+ )
+ visualizer.add_arrow(
+ target_point,
+ heading_end,
+ color=(1.0, 0.2, 0.2, 0.95),
+ width=link_radius * 1.5,
+ label=f"goal_heading_{env_idx}",
+ )
+
+
+@dataclass(kw_only=True)
+class GoalPoseCommandCfg(CommandTermCfg):
+ @dataclass
+ class RadiusBucket:
+ radius_range: tuple[float, float]
+ probability: float
+
+ @dataclass
+ class CommandBucket:
+ radius_range: tuple[float, float]
+ probability: float
+ yaw_range: tuple[float, float] | None = None
+ angle_range: tuple[float, float] | None = None
+
+ entity_name: str
+ resample_on_near_goal: bool = False
+ resample_on_goal_success: bool = False
+ resample_position_tolerance: float = 0.0
+ resample_yaw_tolerance: float = 0.0
+
+ @dataclass
+ class Ranges:
+ target_radius: tuple[float, float]
+ target_yaw: tuple[float, float]
+ target_angle: tuple[float, float] = (-math.pi, math.pi)
+ target_radius_buckets: tuple["GoalPoseCommandCfg.RadiusBucket", ...] | None = None
+ target_command_buckets: tuple["GoalPoseCommandCfg.CommandBucket", ...] | None = None
+
+ @dataclass
+ class VizCfg:
+ z_offset: float = 0.08
+ sphere_radius: float = 0.07
+ heading_scale: float = 0.28
+ link_radius: float = 0.012
+
+ ranges: Ranges
+ viz: VizCfg = field(default_factory=VizCfg)
+
+ def build(self, env) -> GoalPoseCommand:
+ return GoalPoseCommand(self, env)
diff --git a/src/mjlab_playground/tasks/target_location_amp/mdp/curriculums.py b/src/mjlab_playground/tasks/target_location_amp/mdp/curriculums.py
new file mode 100644
index 0000000..82fc117
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/mdp/curriculums.py
@@ -0,0 +1,280 @@
+"""Curriculum helpers local to the target-location AMP task."""
+
+# pyright: reportArgumentType=false, reportAttributeAccessIssue=false
+# pyright: reportOptionalIterable=false
+
+from __future__ import annotations
+
+import math
+from typing import TypedDict, cast
+
+import torch
+
+from .commands import GoalPoseCommandCfg
+
+
+class GoalRadiusBucketStage(TypedDict):
+ radius_range: tuple[float, float]
+ probability: float
+
+
+class GoalCommandBucketStage(TypedDict):
+ radius_range: tuple[float, float]
+ probability: float
+ yaw_range: tuple[float, float] | None
+ angle_range: tuple[float, float] | None
+
+
+class GoalStage(TypedDict):
+ step: int
+ target_radius: tuple[float, float] | None
+ target_radius_buckets: list[GoalRadiusBucketStage] | None
+ target_command_buckets: list[GoalCommandBucketStage] | None
+ target_yaw: tuple[float, float] | None
+ target_angle: tuple[float, float] | None
+ resampling_time_range: tuple[float, float] | None
+ resample_on_near_goal: bool | None
+ resample_on_goal_success: bool | None
+ resample_position_tolerance: float | None
+ resample_yaw_tolerance: float | None
+ goal_position_tolerance: float | None
+ goal_yaw_tolerance: float | None
+ goal_hold_steps: int | None
+ goal_max_lin_speed: float | None
+ goal_max_yaw_rate: float | None
+ goal_min_up_z: float | None
+ near_goal_heading_distance: float | None
+ near_goal_stability_weight: float | None
+ near_goal_stability_distance: float | None
+ near_goal_stability_lin_speed_std: float | None
+ near_goal_stability_yaw_rate_std: float | None
+ near_goal_stability_min_up_z: float | None
+ goal_pose_bonus_weight: float | None
+ joint_target_limits_weight: float | None
+
+
+def _apply_goal_reward_stage(env, stage: GoalStage) -> None:
+ reward_manager = env.reward_manager
+
+ if stage.get("goal_position_tolerance") is not None:
+ position_tolerance = float(stage["goal_position_tolerance"])
+ reward_manager.get_term_cfg("position_progress").params["position_tolerance"] = (
+ position_tolerance
+ )
+ reward_manager.get_term_cfg("yaw_progress").params["position_tolerance"] = (
+ position_tolerance
+ )
+ reward_manager.get_term_cfg("goal_position_bonus").params["position_tolerance"] = (
+ position_tolerance
+ )
+ reward_manager.get_term_cfg("goal_pose_bonus").params["position_tolerance"] = (
+ position_tolerance
+ )
+ reward_manager.get_term_cfg("goal_time_efficiency").params["position_tolerance"] = (
+ position_tolerance
+ )
+
+ if stage.get("goal_yaw_tolerance") is not None:
+ yaw_tolerance = float(stage["goal_yaw_tolerance"])
+ reward_manager.get_term_cfg("position_progress").params["yaw_tolerance"] = (
+ yaw_tolerance
+ )
+ reward_manager.get_term_cfg("yaw_progress").params["yaw_tolerance"] = yaw_tolerance
+ reward_manager.get_term_cfg("goal_position_bonus").params["yaw_soft_tolerance"] = (
+ yaw_tolerance
+ )
+ reward_manager.get_term_cfg("goal_pose_bonus").params["yaw_tolerance"] = (
+ yaw_tolerance
+ )
+ reward_manager.get_term_cfg("goal_time_efficiency").params["yaw_tolerance"] = (
+ yaw_tolerance
+ )
+
+ if stage.get("goal_hold_steps") is not None:
+ hold_steps = int(stage["goal_hold_steps"])
+ reward_manager.get_term_cfg("goal_pose_bonus").params["hold_steps"] = hold_steps
+ reward_manager.get_term_cfg("goal_time_efficiency").params["hold_steps"] = (
+ hold_steps
+ )
+
+ if stage.get("goal_max_lin_speed") is not None:
+ max_lin_speed = float(stage["goal_max_lin_speed"])
+ reward_manager.get_term_cfg("goal_pose_bonus").params["max_lin_speed"] = (
+ max_lin_speed
+ )
+ reward_manager.get_term_cfg("goal_time_efficiency").params["max_lin_speed"] = (
+ max_lin_speed
+ )
+
+ if stage.get("goal_max_yaw_rate") is not None:
+ max_yaw_rate = float(stage["goal_max_yaw_rate"])
+ reward_manager.get_term_cfg("goal_pose_bonus").params["max_yaw_rate"] = max_yaw_rate
+ reward_manager.get_term_cfg("goal_time_efficiency").params["max_yaw_rate"] = (
+ max_yaw_rate
+ )
+
+ if stage.get("goal_min_up_z") is not None:
+ min_up_z = float(stage["goal_min_up_z"])
+ reward_manager.get_term_cfg("goal_pose_bonus").params["min_up_z"] = min_up_z
+ reward_manager.get_term_cfg("goal_time_efficiency").params["min_up_z"] = min_up_z
+
+ if stage.get("near_goal_heading_distance") is not None:
+ near_distance = float(stage["near_goal_heading_distance"])
+ reward_manager.get_term_cfg("near_goal_heading_alignment").params[
+ "near_distance"
+ ] = near_distance
+ reward_manager.get_term_cfg("yaw_progress").params["near_distance"] = near_distance
+
+ if stage.get("near_goal_stability_weight") is not None:
+ reward_manager.get_term_cfg("near_goal_stability").weight = float(
+ stage["near_goal_stability_weight"]
+ )
+
+ if stage.get("near_goal_stability_distance") is not None:
+ reward_manager.get_term_cfg("near_goal_stability").params["near_distance"] = float(
+ stage["near_goal_stability_distance"]
+ )
+
+ if stage.get("near_goal_stability_lin_speed_std") is not None:
+ reward_manager.get_term_cfg("near_goal_stability").params["lin_speed_std"] = float(
+ stage["near_goal_stability_lin_speed_std"]
+ )
+
+ if stage.get("near_goal_stability_yaw_rate_std") is not None:
+ reward_manager.get_term_cfg("near_goal_stability").params["yaw_rate_std"] = float(
+ stage["near_goal_stability_yaw_rate_std"]
+ )
+
+ if stage.get("near_goal_stability_min_up_z") is not None:
+ reward_manager.get_term_cfg("near_goal_stability").params["min_up_z"] = float(
+ stage["near_goal_stability_min_up_z"]
+ )
+
+ if stage.get("goal_pose_bonus_weight") is not None:
+ reward_manager.get_term_cfg("goal_pose_bonus").weight = float(
+ stage["goal_pose_bonus_weight"]
+ )
+
+ if stage.get("joint_target_limits_weight") is not None:
+ reward_manager.get_term_cfg("joint_target_limits").weight = float(
+ stage["joint_target_limits_weight"]
+ )
+
+
+def _radius_range_metrics(
+ cfg: GoalPoseCommandCfg,
+) -> tuple[float, float, torch.Tensor]:
+ if cfg.ranges.target_command_buckets:
+ radius_min = min(
+ float(bucket.radius_range[0]) for bucket in cfg.ranges.target_command_buckets
+ )
+ radius_max = max(
+ float(bucket.radius_range[1]) for bucket in cfg.ranges.target_command_buckets
+ )
+ return (
+ radius_min,
+ radius_max,
+ torch.tensor(float(len(cfg.ranges.target_command_buckets))),
+ )
+
+ if not cfg.ranges.target_radius_buckets:
+ return (
+ float(cfg.ranges.target_radius[0]),
+ float(cfg.ranges.target_radius[1]),
+ torch.tensor(0.0),
+ )
+
+ radius_min = min(
+ float(bucket.radius_range[0]) for bucket in cfg.ranges.target_radius_buckets
+ )
+ radius_max = max(
+ float(bucket.radius_range[1]) for bucket in cfg.ranges.target_radius_buckets
+ )
+ return (
+ radius_min,
+ radius_max,
+ torch.tensor(float(len(cfg.ranges.target_radius_buckets))),
+ )
+
+
+def goal_pose_ranges(
+ env,
+ env_ids: torch.Tensor,
+ command_name: str,
+ stages: list[GoalStage],
+) -> dict[str, torch.Tensor]:
+ del env_ids
+ command_term = env.command_manager.get_term(command_name)
+ cfg = cast(GoalPoseCommandCfg, command_term.cfg)
+ reward_manager = env.reward_manager
+ for stage in stages:
+ if env.common_step_counter > stage["step"]:
+ if stage.get("target_radius") is not None:
+ cfg.ranges.target_radius = stage["target_radius"]
+ if stage.get("target_radius_buckets") is not None:
+ cfg.ranges.target_radius_buckets = tuple(
+ GoalPoseCommandCfg.RadiusBucket(
+ radius_range=tuple(bucket["radius_range"]),
+ probability=float(bucket["probability"]),
+ )
+ for bucket in stage["target_radius_buckets"]
+ )
+ if stage.get("target_command_buckets") is not None:
+ cfg.ranges.target_command_buckets = tuple(
+ GoalPoseCommandCfg.CommandBucket(
+ radius_range=tuple(bucket["radius_range"]),
+ probability=float(bucket["probability"]),
+ yaw_range=(
+ tuple(bucket["yaw_range"]) if bucket["yaw_range"] is not None else None
+ ),
+ angle_range=(
+ tuple(bucket["angle_range"])
+ if bucket["angle_range"] is not None
+ else None
+ ),
+ )
+ for bucket in stage["target_command_buckets"]
+ )
+ if stage.get("target_yaw") is not None:
+ cfg.ranges.target_yaw = stage["target_yaw"]
+ if stage.get("target_angle") is not None:
+ cfg.ranges.target_angle = stage["target_angle"]
+ if stage.get("resampling_time_range") is not None:
+ cfg.resampling_time_range = stage["resampling_time_range"]
+ if stage.get("resample_on_near_goal") is not None:
+ cfg.resample_on_near_goal = stage["resample_on_near_goal"]
+ if stage.get("resample_on_goal_success") is not None:
+ cfg.resample_on_goal_success = stage["resample_on_goal_success"]
+ if stage.get("resample_position_tolerance") is not None:
+ cfg.resample_position_tolerance = stage["resample_position_tolerance"]
+ if stage.get("resample_yaw_tolerance") is not None:
+ cfg.resample_yaw_tolerance = stage["resample_yaw_tolerance"]
+ _apply_goal_reward_stage(env, stage)
+
+ radius_min, radius_max, bucket_count = _radius_range_metrics(cfg)
+ return {
+ "target_radius_min": torch.tensor(radius_min),
+ "target_radius_max": torch.tensor(radius_max),
+ "target_radius_bucket_count": bucket_count,
+ "target_yaw_min": torch.tensor(cfg.ranges.target_yaw[0]),
+ "target_yaw_max": torch.tensor(cfg.ranges.target_yaw[1]),
+ "target_angle_min": torch.tensor(cfg.ranges.target_angle[0]),
+ "target_angle_max": torch.tensor(cfg.ranges.target_angle[1]),
+ "resampling_time_min": torch.tensor(cfg.resampling_time_range[0]),
+ "resampling_time_max": torch.tensor(cfg.resampling_time_range[1]),
+ "resample_on_near_goal": torch.tensor(float(cfg.resample_on_near_goal)),
+ "resample_on_goal_success": torch.tensor(float(cfg.resample_on_goal_success)),
+ "resample_position_tolerance": torch.tensor(cfg.resample_position_tolerance),
+ "resample_yaw_tolerance": torch.tensor(cfg.resample_yaw_tolerance),
+ "reward_goal_position_tolerance": torch.tensor(
+ reward_manager.get_term_cfg("goal_pose_bonus").params["position_tolerance"]
+ ),
+ "reward_goal_yaw_tolerance_deg": torch.tensor(
+ math.degrees(
+ reward_manager.get_term_cfg("goal_pose_bonus").params["yaw_tolerance"]
+ )
+ ),
+ "reward_goal_hold_steps": torch.tensor(
+ reward_manager.get_term_cfg("goal_pose_bonus").params["hold_steps"]
+ ),
+ }
diff --git a/src/mjlab_playground/tasks/target_location_amp/mdp/observations.py b/src/mjlab_playground/tasks/target_location_amp/mdp/observations.py
new file mode 100644
index 0000000..6def890
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/mdp/observations.py
@@ -0,0 +1,27 @@
+"""Goal-pose observations for target-location AMP locomotion."""
+
+from __future__ import annotations
+
+import torch
+
+
+def goal_local_position(env, command_name: str = "goal_pose") -> torch.Tensor:
+ command_term = env.command_manager.get_term(command_name)
+ return command_term.local_pos_error
+
+
+def heading_to_target_features(env, command_name: str = "goal_pose") -> torch.Tensor:
+ command_term = env.command_manager.get_term(command_name)
+ angle = command_term.heading_to_target
+ return torch.stack((torch.sin(angle), torch.cos(angle)), dim=-1)
+
+
+def final_yaw_error_features(env, command_name: str = "goal_pose") -> torch.Tensor:
+ command_term = env.command_manager.get_term(command_name)
+ angle = command_term.final_heading_error
+ return torch.stack((torch.sin(angle), torch.cos(angle)), dim=-1)
+
+
+def goal_distance(env, command_name: str = "goal_pose") -> torch.Tensor:
+ command_term = env.command_manager.get_term(command_name)
+ return command_term.distance_to_goal.unsqueeze(-1)
diff --git a/src/mjlab_playground/tasks/target_location_amp/mdp/rewards.py b/src/mjlab_playground/tasks/target_location_amp/mdp/rewards.py
new file mode 100644
index 0000000..c0b7784
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/mdp/rewards.py
@@ -0,0 +1,558 @@
+"""Rewards local to the target-location AMP locomotion task."""
+
+from __future__ import annotations
+
+import torch
+from mjlab.managers.scene_entity_config import SceneEntityCfg
+
+from .state import update_goal_pose_state
+
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+
+
+def _log_metric(env, key: str, value: torch.Tensor) -> None:
+ extras = getattr(env, "extras", None)
+ if not isinstance(extras, dict):
+ return
+ log = extras.get("log")
+ if isinstance(log, dict):
+ log[key] = value
+
+
+def _log_goal_pose_metrics(
+ env,
+ command_term,
+ *,
+ position_tolerance: float,
+ yaw_tolerance: float,
+ hold_steps: torch.Tensor,
+) -> None:
+ yaw_abs = torch.abs(command_term.final_heading_error)
+ position_ok = command_term.distance_to_goal <= position_tolerance
+ yaw_ok = yaw_abs <= yaw_tolerance
+ pose_ok = position_ok & yaw_ok
+ _log_metric(
+ env,
+ "Metrics/target_location/reward_goal_distance",
+ command_term.distance_to_goal.mean(),
+ )
+ _log_metric(env, "Metrics/target_location/reward_final_yaw_abs", yaw_abs.mean())
+ _log_metric(
+ env, "Metrics/target_location/position_ok_fraction", position_ok.float().mean()
+ )
+ _log_metric(env, "Metrics/target_location/yaw_ok_fraction", yaw_ok.float().mean())
+ _log_metric(env, "Metrics/target_location/pose_ok_fraction", pose_ok.float().mean())
+ _log_metric(env, "Metrics/target_location/hold_steps_mean", hold_steps.float().mean())
+
+
+def _state(env, command_name: str, position_tolerance: float, yaw_tolerance: float):
+ command_term = env.command_manager.get_term(command_name)
+ state = update_goal_pose_state(
+ env,
+ distance=command_term.distance_to_goal,
+ final_yaw_error=command_term.final_heading_error,
+ position_tolerance=position_tolerance,
+ yaw_tolerance=yaw_tolerance,
+ )
+ _log_goal_pose_metrics(
+ env,
+ command_term,
+ position_tolerance=position_tolerance,
+ yaw_tolerance=yaw_tolerance,
+ hold_steps=state[2],
+ )
+ return state
+
+
+def _healthy_reward_gate(
+ env,
+ reward: torch.Tensor,
+ *,
+ entity_name: str = "robot",
+ min_root_height: float | None = None,
+ min_up_z: float | None = None,
+) -> torch.Tensor:
+ if min_root_height is None and min_up_z is None:
+ return reward
+
+ asset = env.scene[entity_name]
+ stable = torch.ones_like(reward, dtype=torch.bool)
+ if min_root_height is not None:
+ stable = stable & (asset.data.root_link_pos_w[:, 2] >= float(min_root_height))
+ if min_up_z is not None:
+ up_z = -asset.data.projected_gravity_b[:, 2]
+ stable = stable & (up_z >= float(min_up_z))
+ _log_metric(env, "Metrics/target_location/reward_gate_up_z", up_z.mean())
+ _log_metric(
+ env, "Metrics/target_location/reward_gate_fraction", stable.float().mean()
+ )
+ # Keep negative shaping when unstable, but suppress positive reward hacking.
+ return torch.where(stable, reward, torch.minimum(reward, torch.zeros_like(reward)))
+
+
+def _apply_training_decay(
+ env,
+ reward: torch.Tensor,
+ *,
+ start_step: int = 0,
+ decay_steps: int = 0,
+ final_scale: float = 1.0,
+ log_name: str | None = None,
+) -> torch.Tensor:
+ final = float(final_scale)
+ if start_step <= 0 and decay_steps <= 0 and final >= 1.0:
+ return reward
+
+ step = int(getattr(env, "common_step_counter", 0))
+ start = int(start_step)
+ decay = int(decay_steps)
+ if step < start:
+ scale = 1.0
+ elif decay <= 0:
+ scale = final
+ else:
+ progress = min(1.0, (step - start) / float(decay))
+ scale = 1.0 - (1.0 - final) * progress
+
+ scale_tensor = torch.as_tensor(scale, dtype=reward.dtype, device=reward.device)
+ if log_name is not None:
+ _log_metric(env, f"Curriculum/target_location/{log_name}_scale", scale_tensor)
+ return reward * scale_tensor
+
+
+def alive_reward(
+ env,
+ decay_start_step: int = 0,
+ decay_steps: int = 0,
+ final_scale: float = 1.0,
+) -> torch.Tensor:
+ """Dense survival reward so early PPO gets a clear non-fall signal."""
+ reward = torch.ones(env.num_envs, device=env.device)
+ return _apply_training_decay(
+ env,
+ reward,
+ start_step=decay_start_step,
+ decay_steps=decay_steps,
+ final_scale=final_scale,
+ log_name="alive",
+ )
+
+
+def upright_orientation_reward(
+ env,
+ std: float = 0.5,
+ decay_start_step: int = 0,
+ decay_steps: int = 0,
+ final_scale: float = 1.0,
+ asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> torch.Tensor:
+ """Reward keeping the root upright from projected gravity."""
+ asset = env.scene[asset_cfg.name]
+ gravity_xy = asset.data.projected_gravity_b[:, :2]
+ error = torch.sum(torch.square(gravity_xy), dim=-1)
+ reward = torch.exp(-error / max(float(std) ** 2, 1.0e-6))
+ reward = _apply_training_decay(
+ env,
+ reward,
+ start_step=decay_start_step,
+ decay_steps=decay_steps,
+ final_scale=final_scale,
+ log_name="upright",
+ )
+ _log_metric(env, "Metrics/target_location/upright_reward", reward.mean())
+ return reward
+
+
+def root_height_reward(
+ env,
+ target_height: float = 0.65,
+ std: float = 0.2,
+ decay_start_step: int = 0,
+ decay_steps: int = 0,
+ final_scale: float = 1.0,
+ asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> torch.Tensor:
+ """Reward staying near the nominal root height."""
+ asset = env.scene[asset_cfg.name]
+ height = asset.data.root_link_pos_w[:, 2]
+ error = torch.square(height - float(target_height))
+ reward = torch.exp(-error / max(float(std) ** 2, 1.0e-6))
+ reward = _apply_training_decay(
+ env,
+ reward,
+ start_step=decay_start_step,
+ decay_steps=decay_steps,
+ final_scale=final_scale,
+ log_name="root_height",
+ )
+ _log_metric(env, "Metrics/target_location/root_height_reward", reward.mean())
+ return reward
+
+
+def body_ang_vel_xy_l2(
+ env,
+ asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> torch.Tensor:
+ """Penalize roll/pitch angular velocity of the root."""
+ asset = env.scene[asset_cfg.name]
+ return torch.sum(torch.square(asset.data.root_link_ang_vel_b[:, :2]), dim=-1)
+
+
+def action_rate_l2(env) -> torch.Tensor:
+ """Penalize raw policy action changes."""
+ return torch.sum(
+ torch.square(env.action_manager.action - env.action_manager.prev_action),
+ dim=-1,
+ )
+
+
+def joint_pos_limits(
+ env,
+ asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> torch.Tensor:
+ """Penalize excursions beyond soft joint position limits."""
+ asset = env.scene[asset_cfg.name]
+ soft_limits = asset.data.soft_joint_pos_limits
+ joint_pos = asset.data.joint_pos[:, asset_cfg.joint_ids]
+ lower_limits = soft_limits[:, asset_cfg.joint_ids, 0]
+ upper_limits = soft_limits[:, asset_cfg.joint_ids, 1]
+ out_of_limits = -(joint_pos - lower_limits).clip(max=0.0)
+ out_of_limits += (joint_pos - upper_limits).clip(min=0.0)
+ return torch.sum(out_of_limits, dim=-1)
+
+
+def joint_target_pos_limits(
+ env,
+ asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+ action_name: str = "joint_pos",
+ normalize_by_range: bool = False,
+) -> torch.Tensor:
+ """Penalize commanded position targets beyond soft joint limits."""
+ asset = env.scene[asset_cfg.name]
+ term = env.action_manager.get_term(action_name)
+ target_ids = term.target_ids
+ soft_limits = asset.data.soft_joint_pos_limits[:, target_ids]
+ lower_limits = soft_limits[:, :, 0]
+ upper_limits = soft_limits[:, :, 1]
+
+ processed_actions = term.raw_action * term.scale + term.offset
+ if term.__class__.__name__ == "RelativeJointPositionAction":
+ joint_pos = asset.data.joint_pos[:, target_ids]
+ target_pos = joint_pos + processed_actions
+ else:
+ target_pos = processed_actions
+
+ out_of_limits = -(target_pos - lower_limits).clip(max=0.0)
+ out_of_limits += (target_pos - upper_limits).clip(min=0.0)
+ if normalize_by_range:
+ limit_range = (upper_limits - lower_limits).clamp_min(1.0e-6)
+ out_of_limits = out_of_limits / limit_range
+ penalty = torch.sum(out_of_limits, dim=-1)
+ metric_name = (
+ "Metrics/target_location/target_excess_normalized"
+ if normalize_by_range
+ else "Metrics/target_location/target_excess"
+ )
+ _log_metric(env, metric_name, penalty.mean())
+ return penalty
+
+
+def position_progress_reward(
+ env,
+ command_name: str = "goal_pose",
+ position_tolerance: float = 0.1,
+ yaw_tolerance: float = 0.1,
+ max_speed: float = 2.5,
+ entity_name: str = "robot",
+ min_root_height: float | None = None,
+ min_up_z: float | None = None,
+) -> torch.Tensor:
+ distance_progress, _, _ = _state(env, command_name, position_tolerance, yaw_tolerance)
+ reward = distance_progress / max(float(env.step_dt) * max(max_speed, 1.0e-6), 1.0e-6)
+ return _healthy_reward_gate(
+ env,
+ reward,
+ entity_name=entity_name,
+ min_root_height=min_root_height,
+ min_up_z=min_up_z,
+ )
+
+
+def distance_error_penalty(
+ env,
+ command_name: str = "goal_pose",
+ min_distance_scale: float = 0.5,
+) -> torch.Tensor:
+ command_term = env.command_manager.get_term(command_name)
+ scale = torch.clamp(
+ command_term.initial_distance_to_goal,
+ min=min_distance_scale,
+ )
+ distance_error = command_term.distance_to_goal / scale
+ _log_metric(env, "Metrics/target_location/distance_error_norm", distance_error.mean())
+ return distance_error
+
+
+def yaw_progress_reward(
+ env,
+ command_name: str = "goal_pose",
+ position_tolerance: float = 0.1,
+ yaw_tolerance: float = 0.1,
+ near_distance: float = 0.3,
+ entity_name: str = "robot",
+ min_root_height: float | None = None,
+ min_up_z: float | None = None,
+) -> torch.Tensor:
+ _, yaw_progress, _ = _state(env, command_name, position_tolerance, yaw_tolerance)
+ command_term = env.command_manager.get_term(command_name)
+ near = (command_term.distance_to_goal <= near_distance).float()
+ reward = yaw_progress * near
+ return _healthy_reward_gate(
+ env,
+ reward,
+ entity_name=entity_name,
+ min_root_height=min_root_height,
+ min_up_z=min_up_z,
+ )
+
+
+def goal_position_bonus(
+ env,
+ command_name: str = "goal_pose",
+ position_tolerance: float = 0.1,
+ yaw_soft_tolerance: float | None = None,
+ entity_name: str = "robot",
+ min_root_height: float | None = None,
+ min_up_z: float | None = None,
+) -> torch.Tensor:
+ command_term = env.command_manager.get_term(command_name)
+ position_ok = command_term.distance_to_goal <= position_tolerance
+ if yaw_soft_tolerance is None:
+ reward = position_ok.float()
+ else:
+ yaw_ok = torch.abs(command_term.final_heading_error) <= yaw_soft_tolerance
+ reward = (position_ok & yaw_ok).float()
+ return _healthy_reward_gate(
+ env,
+ reward,
+ entity_name=entity_name,
+ min_root_height=min_root_height,
+ min_up_z=min_up_z,
+ )
+
+
+def near_goal_heading_alignment_reward(
+ env,
+ command_name: str = "goal_pose",
+ near_distance: float = 0.5,
+ entity_name: str = "robot",
+ min_root_height: float | None = None,
+ min_up_z: float | None = None,
+) -> torch.Tensor:
+ command_term = env.command_manager.get_term(command_name)
+ near_weight = torch.exp(
+ -command_term.distance_to_goal / max(float(near_distance), 1.0e-6)
+ )
+ reward = near_weight * torch.cos(command_term.final_heading_error)
+ _log_metric(env, "Metrics/target_location/near_heading_alignment", reward.mean())
+ return _healthy_reward_gate(
+ env,
+ reward,
+ entity_name=entity_name,
+ min_root_height=min_root_height,
+ min_up_z=min_up_z,
+ )
+
+
+def goal_stability_mask(
+ env,
+ *,
+ entity_name: str = "robot",
+ max_lin_speed: float | None = None,
+ max_yaw_rate: float | None = None,
+ min_up_z: float | None = None,
+) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
+ asset = env.scene[entity_name]
+ device = asset.data.root_link_pos_w.device
+ stable = torch.ones(
+ asset.data.root_link_pos_w.shape[0], dtype=torch.bool, device=device
+ )
+ metrics: dict[str, torch.Tensor] = {}
+
+ if max_lin_speed is not None:
+ lin_speed = torch.linalg.vector_norm(asset.data.root_link_lin_vel_b[:, :2], dim=1)
+ metrics["lin_speed"] = lin_speed
+ stable = stable & (lin_speed <= float(max_lin_speed))
+
+ if max_yaw_rate is not None:
+ yaw_rate = torch.abs(asset.data.root_link_ang_vel_b[:, 2])
+ metrics["yaw_rate"] = yaw_rate
+ stable = stable & (yaw_rate <= float(max_yaw_rate))
+
+ if min_up_z is not None:
+ up_z = -asset.data.projected_gravity_b[:, 2]
+ metrics["up_z"] = up_z
+ stable = stable & (up_z >= float(min_up_z))
+
+ metrics["stable"] = stable.float()
+ return stable, metrics
+
+
+def _stability_ok(
+ env,
+ *,
+ entity_name: str,
+ max_lin_speed: float | None,
+ max_yaw_rate: float | None,
+ min_up_z: float | None,
+) -> torch.Tensor | None:
+ if max_lin_speed is None and max_yaw_rate is None and min_up_z is None:
+ return None
+
+ stable, metrics = goal_stability_mask(
+ env,
+ entity_name=entity_name,
+ max_lin_speed=max_lin_speed,
+ max_yaw_rate=max_yaw_rate,
+ min_up_z=min_up_z,
+ )
+ if "lin_speed" in metrics:
+ _log_metric(
+ env,
+ "Metrics/target_location/stability_lin_speed",
+ metrics["lin_speed"].mean(),
+ )
+ if "yaw_rate" in metrics:
+ _log_metric(
+ env,
+ "Metrics/target_location/stability_yaw_rate",
+ metrics["yaw_rate"].mean(),
+ )
+ if "up_z" in metrics:
+ _log_metric(env, "Metrics/target_location/stability_up_z", metrics["up_z"].mean())
+ _log_metric(
+ env, "Metrics/target_location/stability_ok_fraction", stable.float().mean()
+ )
+ return stable
+
+
+def goal_pose_terminal_bonus(
+ env,
+ command_name: str = "goal_pose",
+ position_tolerance: float = 0.1,
+ yaw_tolerance: float = 0.1,
+ hold_steps: int = 10,
+ entity_name: str = "robot",
+ max_lin_speed: float | None = None,
+ max_yaw_rate: float | None = None,
+ min_up_z: float | None = None,
+) -> torch.Tensor:
+ from .state import update_goal_pose_success_hold_state
+
+ command_term = env.command_manager.get_term(command_name)
+ stable = _stability_ok(
+ env,
+ entity_name=entity_name,
+ max_lin_speed=max_lin_speed,
+ max_yaw_rate=max_yaw_rate,
+ min_up_z=min_up_z,
+ )
+ success_now = (command_term.distance_to_goal <= float(position_tolerance)) & (
+ torch.abs(command_term.final_heading_error) <= float(yaw_tolerance)
+ )
+ if stable is not None:
+ success_now = success_now & stable
+ hold = update_goal_pose_success_hold_state(env, success_now=success_now)
+ just_reached = hold == hold_steps
+ _log_metric(
+ env,
+ "Metrics/target_location/terminal_success_fraction",
+ just_reached.float().mean(),
+ )
+ return just_reached.float()
+
+
+def goal_time_efficiency_bonus(
+ env,
+ command_name: str = "goal_pose",
+ position_tolerance: float = 0.1,
+ yaw_tolerance: float = 0.1,
+ hold_steps: int = 10,
+ max_speed: float = 2.5,
+ entity_name: str = "robot",
+ max_lin_speed: float | None = None,
+ max_yaw_rate: float | None = None,
+ min_up_z: float | None = None,
+) -> torch.Tensor:
+ from .state import update_goal_pose_success_hold_state
+
+ command_term = env.command_manager.get_term(command_name)
+ stable = _stability_ok(
+ env,
+ entity_name=entity_name,
+ max_lin_speed=max_lin_speed,
+ max_yaw_rate=max_yaw_rate,
+ min_up_z=min_up_z,
+ )
+ success_now = (command_term.distance_to_goal <= float(position_tolerance)) & (
+ torch.abs(command_term.final_heading_error) <= float(yaw_tolerance)
+ )
+ if stable is not None:
+ success_now = success_now & stable
+ hold = update_goal_pose_success_hold_state(env, success_now=success_now)
+ just_reached = hold == hold_steps
+
+ optimal_time = command_term.initial_distance_to_goal / max(max_speed, 1.0e-6)
+ actual_steps = env.episode_length_buf.float() - float(max(hold_steps - 1, 0))
+ actual_steps = torch.clamp(actual_steps, min=1.0)
+ actual_time = actual_steps * float(env.step_dt)
+
+ efficiency = torch.clamp(optimal_time / actual_time, min=0.0, max=1.0)
+ bonus = torch.where(just_reached, efficiency, torch.zeros_like(efficiency))
+ _log_metric(
+ env,
+ "Metrics/target_location/time_efficiency_on_success",
+ torch.where(just_reached, efficiency, torch.zeros_like(efficiency)).mean(),
+ )
+ return bonus
+
+
+def near_goal_stability_reward(
+ env,
+ command_name: str = "goal_pose",
+ entity_name: str = "robot",
+ near_distance: float = 0.5,
+ position_tolerance: float | None = None,
+ yaw_tolerance: float | None = None,
+ lin_speed_std: float = 0.35,
+ yaw_rate_std: float = 0.6,
+ min_up_z: float = 0.75,
+) -> torch.Tensor:
+ command_term = env.command_manager.get_term(command_name)
+ asset = env.scene[entity_name]
+ lin_speed = torch.linalg.vector_norm(asset.data.root_link_lin_vel_b[:, :2], dim=1)
+ yaw_rate = torch.abs(asset.data.root_link_ang_vel_b[:, 2])
+ up_z = -asset.data.projected_gravity_b[:, 2]
+
+ near_weight = torch.exp(
+ -command_term.distance_to_goal / max(float(near_distance), 1.0e-6)
+ )
+ lin_score = torch.exp(-torch.square(lin_speed / max(float(lin_speed_std), 1.0e-6)))
+ yaw_score = torch.exp(-torch.square(yaw_rate / max(float(yaw_rate_std), 1.0e-6)))
+ upright_score = torch.clamp(
+ (up_z - float(min_up_z)) / max(1.0 - float(min_up_z), 1.0e-6),
+ min=0.0,
+ max=1.0,
+ )
+ reward = near_weight * lin_score * yaw_score * upright_score
+ if position_tolerance is not None:
+ reward = (
+ reward * (command_term.distance_to_goal <= float(position_tolerance)).float()
+ )
+ if yaw_tolerance is not None:
+ reward = (
+ reward
+ * (torch.abs(command_term.final_heading_error) <= float(yaw_tolerance)).float()
+ )
+ _log_metric(env, "Metrics/target_location/near_stability_reward", reward.mean())
+ return reward
diff --git a/src/mjlab_playground/tasks/target_location_amp/mdp/state.py b/src/mjlab_playground/tasks/target_location_amp/mdp/state.py
new file mode 100644
index 0000000..dfc918a
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/mdp/state.py
@@ -0,0 +1,149 @@
+"""Goal-pose progress state for target-location AMP locomotion."""
+
+from __future__ import annotations
+
+import torch
+
+
+def ensure_goal_pose_state(env) -> None:
+ if not hasattr(env, "_goal_pose_prev_distance"):
+ env._goal_pose_prev_distance = torch.zeros(
+ env.num_envs, dtype=torch.float32, device=env.device
+ )
+ if not hasattr(env, "_goal_pose_prev_yaw_abs"):
+ env._goal_pose_prev_yaw_abs = torch.zeros(
+ env.num_envs, dtype=torch.float32, device=env.device
+ )
+ if not hasattr(env, "_goal_pose_distance_progress"):
+ env._goal_pose_distance_progress = torch.zeros(
+ env.num_envs, dtype=torch.float32, device=env.device
+ )
+ if not hasattr(env, "_goal_pose_yaw_progress"):
+ env._goal_pose_yaw_progress = torch.zeros(
+ env.num_envs, dtype=torch.float32, device=env.device
+ )
+ if not hasattr(env, "_goal_pose_hold_steps"):
+ env._goal_pose_hold_steps = torch.zeros(
+ env.num_envs, dtype=torch.long, device=env.device
+ )
+ if not hasattr(env, "_goal_pose_success_hold_steps"):
+ env._goal_pose_success_hold_steps = torch.zeros(
+ env.num_envs, dtype=torch.long, device=env.device
+ )
+ if not hasattr(env, "_goal_pose_success_hold_last_step"):
+ env._goal_pose_success_hold_last_step = torch.full(
+ (env.num_envs,), -1, dtype=torch.long, device=env.device
+ )
+ if not hasattr(env, "_goal_pose_state_last_step"):
+ env._goal_pose_state_last_step = torch.full(
+ (env.num_envs,), -1, dtype=torch.long, device=env.device
+ )
+ if not hasattr(env, "_goal_pose_just_resampled"):
+ env._goal_pose_just_resampled = torch.zeros(
+ env.num_envs, dtype=torch.bool, device=env.device
+ )
+
+
+def reset_goal_pose_state(env, env_ids: torch.Tensor) -> None:
+ ensure_goal_pose_state(env)
+ env._goal_pose_prev_distance[env_ids] = 0.0
+ env._goal_pose_prev_yaw_abs[env_ids] = 0.0
+ env._goal_pose_distance_progress[env_ids] = 0.0
+ env._goal_pose_yaw_progress[env_ids] = 0.0
+ env._goal_pose_hold_steps[env_ids] = 0
+ env._goal_pose_success_hold_steps[env_ids] = 0
+ env._goal_pose_success_hold_last_step[env_ids] = -1
+ env._goal_pose_state_last_step[env_ids] = -1
+ env._goal_pose_just_resampled[env_ids] = True
+
+
+def update_goal_pose_state(
+ env,
+ *,
+ distance: torch.Tensor,
+ final_yaw_error: torch.Tensor,
+ position_tolerance: float,
+ yaw_tolerance: float,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ ensure_goal_pose_state(env)
+
+ current_step = env.episode_length_buf
+ step_changed = current_step != env._goal_pose_state_last_step
+ if torch.any(step_changed):
+ yaw_abs = torch.abs(final_yaw_error)
+ skip_mask = (current_step <= 1) | env._goal_pose_just_resampled
+
+ distance_progress = env._goal_pose_prev_distance - distance
+ yaw_progress = env._goal_pose_prev_yaw_abs - yaw_abs
+ distance_progress = torch.where(
+ skip_mask,
+ torch.zeros_like(distance_progress),
+ distance_progress,
+ )
+ yaw_progress = torch.where(
+ skip_mask,
+ torch.zeros_like(yaw_progress),
+ yaw_progress,
+ )
+
+ position_ok = distance <= position_tolerance
+ yaw_ok = yaw_abs <= yaw_tolerance
+ goal_ok = position_ok & yaw_ok
+ next_hold_steps = torch.where(
+ goal_ok,
+ env._goal_pose_hold_steps + 1,
+ torch.zeros_like(env._goal_pose_hold_steps),
+ )
+
+ env._goal_pose_distance_progress = torch.where(
+ step_changed, distance_progress, env._goal_pose_distance_progress
+ )
+ env._goal_pose_yaw_progress = torch.where(
+ step_changed, yaw_progress, env._goal_pose_yaw_progress
+ )
+ env._goal_pose_hold_steps = torch.where(
+ step_changed, next_hold_steps, env._goal_pose_hold_steps
+ )
+ env._goal_pose_prev_distance = torch.where(
+ step_changed, distance, env._goal_pose_prev_distance
+ )
+ env._goal_pose_prev_yaw_abs = torch.where(
+ step_changed, yaw_abs, env._goal_pose_prev_yaw_abs
+ )
+ env._goal_pose_state_last_step = torch.where(
+ step_changed, current_step, env._goal_pose_state_last_step
+ )
+ env._goal_pose_just_resampled[step_changed] = False
+
+ return (
+ env._goal_pose_distance_progress,
+ env._goal_pose_yaw_progress,
+ env._goal_pose_hold_steps,
+ )
+
+
+def update_goal_pose_success_hold_state(
+ env,
+ *,
+ success_now: torch.Tensor,
+) -> torch.Tensor:
+ """Count consecutive controller steps satisfying the full success contract."""
+ ensure_goal_pose_state(env)
+ current_step = env.episode_length_buf
+ step_changed = current_step != env._goal_pose_success_hold_last_step
+ next_hold_steps = torch.where(
+ success_now,
+ env._goal_pose_success_hold_steps + 1,
+ torch.zeros_like(env._goal_pose_success_hold_steps),
+ )
+ env._goal_pose_success_hold_steps = torch.where(
+ step_changed,
+ next_hold_steps,
+ env._goal_pose_success_hold_steps,
+ )
+ env._goal_pose_success_hold_last_step = torch.where(
+ step_changed,
+ current_step,
+ env._goal_pose_success_hold_last_step,
+ )
+ return env._goal_pose_success_hold_steps
diff --git a/src/mjlab_playground/tasks/target_location_amp/mdp/terminations.py b/src/mjlab_playground/tasks/target_location_amp/mdp/terminations.py
new file mode 100644
index 0000000..2812af6
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/mdp/terminations.py
@@ -0,0 +1,34 @@
+"""Terminations local to the target-location AMP locomotion task."""
+
+from __future__ import annotations
+
+from .rewards import goal_stability_mask
+from .state import update_goal_pose_success_hold_state
+
+
+def goal_pose_reached(
+ env,
+ command_name: str = "goal_pose",
+ position_tolerance: float = 0.1,
+ yaw_tolerance: float = 0.2,
+ hold_steps: int = 10,
+ entity_name: str = "robot",
+ max_lin_speed: float | None = None,
+ max_yaw_rate: float | None = None,
+ min_up_z: float | None = None,
+):
+ command_term = env.command_manager.get_term(command_name)
+ success_now = (command_term.distance_to_goal <= float(position_tolerance)) & (
+ abs(command_term.final_heading_error) <= float(yaw_tolerance)
+ )
+ if max_lin_speed is not None or max_yaw_rate is not None or min_up_z is not None:
+ stable, _ = goal_stability_mask(
+ env,
+ entity_name=entity_name,
+ max_lin_speed=max_lin_speed,
+ max_yaw_rate=max_yaw_rate,
+ min_up_z=min_up_z,
+ )
+ success_now = success_now & stable
+ hold = update_goal_pose_success_hold_state(env, success_now=success_now)
+ return hold >= hold_steps
diff --git a/src/mjlab_playground/tasks/target_location_amp/target_location_amp_env_cfg.py b/src/mjlab_playground/tasks/target_location_amp/target_location_amp_env_cfg.py
new file mode 100644
index 0000000..ccd35da
--- /dev/null
+++ b/src/mjlab_playground/tasks/target_location_amp/target_location_amp_env_cfg.py
@@ -0,0 +1,279 @@
+"""Goal-conditioned K1 locomotion AMP environment configuration."""
+
+from __future__ import annotations
+
+import math
+
+from mjlab.envs import ManagerBasedRlEnvCfg
+from mjlab.envs.mdp.actions import RelativeJointPositionActionCfg
+from mjlab.managers.action_manager import ActionTermCfg
+from mjlab.managers.event_manager import EventTermCfg
+from mjlab.managers.metrics_manager import MetricsTermCfg
+from mjlab.managers.observation_manager import ObservationGroupCfg, ObservationTermCfg
+from mjlab.managers.reward_manager import RewardTermCfg
+from mjlab.managers.termination_manager import TerminationTermCfg
+from mjlab.scene import SceneCfg
+from mjlab.sim import MujocoCfg, SimulationCfg
+from mjlab.terrains import TerrainEntityCfg
+from mjlab.utils.noise import UniformNoiseCfg as Unoise
+from mjlab.viewer import ViewerConfig
+
+from mjlab_playground.amp import mdp as amp_mdp
+from mjlab_playground.tasks.common import reset_joints_by_offset
+from mjlab_playground.tasks.target_location_amp import mdp as target_mdp
+
+_TARGET_POSITION_TOLERANCE = 0.16
+_TARGET_YAW_TOLERANCE = 0.20
+_TARGET_STABLE_HOLD_STEPS = 25
+_TARGET_MAX_LIN_SPEED = 0.35
+_TARGET_MAX_YAW_RATE = 0.60
+_TARGET_MIN_UP_Z = 0.75
+
+
+def make_target_location_amp_env_cfg(play: bool = False) -> ManagerBasedRlEnvCfg:
+ actor_terms = {
+ "base_ang_vel": ObservationTermCfg(
+ func=amp_mdp.base_ang_vel,
+ noise=Unoise(n_min=-0.15, n_max=0.15),
+ ),
+ "projected_gravity": ObservationTermCfg(
+ func=amp_mdp.projected_gravity,
+ noise=Unoise(n_min=-0.04, n_max=0.04),
+ ),
+ "joint_pos": ObservationTermCfg(
+ func=amp_mdp.joint_pos_rel,
+ noise=Unoise(n_min=-0.015, n_max=0.015),
+ ),
+ "joint_vel": ObservationTermCfg(
+ func=amp_mdp.joint_vel_rel,
+ noise=Unoise(n_min=-0.5, n_max=0.5),
+ ),
+ "actions": ObservationTermCfg(func=amp_mdp.last_action),
+ "goal_local_pos": ObservationTermCfg(
+ func=target_mdp.goal_local_position,
+ noise=Unoise(n_min=-0.01, n_max=0.01),
+ ),
+ "heading_to_target": ObservationTermCfg(
+ func=target_mdp.heading_to_target_features,
+ noise=Unoise(n_min=-0.01, n_max=0.01),
+ ),
+ "final_yaw_error": ObservationTermCfg(
+ func=target_mdp.final_yaw_error_features,
+ noise=Unoise(n_min=-0.01, n_max=0.01),
+ ),
+ "goal_distance": ObservationTermCfg(
+ func=target_mdp.goal_distance,
+ scale=0.25,
+ ),
+ }
+
+ critic_terms = {
+ **actor_terms,
+ "base_lin_vel": ObservationTermCfg(func=amp_mdp.base_lin_vel),
+ "amp_robot": ObservationTermCfg(func=amp_mdp.amp_robot_obs),
+ }
+
+ actions: dict[str, ActionTermCfg] = {
+ "joint_pos": RelativeJointPositionActionCfg(
+ entity_name="robot",
+ actuator_names=(".*",),
+ scale=0.35,
+ )
+ }
+
+ events = {
+ "reset_robot": EventTermCfg(
+ func=amp_mdp.reset_robot_near_origin,
+ mode="reset",
+ params={
+ "robot_name": "robot",
+ "x_range": (-0.04, 0.04),
+ "y_range": (-0.04, 0.04),
+ "yaw_range": (-0.20, 0.20),
+ },
+ ),
+ "reset_robot_joints": EventTermCfg(
+ func=reset_joints_by_offset,
+ mode="reset",
+ params={
+ "position_range": (-0.01, 0.01),
+ "velocity_range": (-0.02, 0.02),
+ },
+ ),
+ }
+
+ rewards = {
+ "distance_error": RewardTermCfg(
+ func=target_mdp.distance_error_penalty,
+ weight=0.0,
+ params={
+ "command_name": "goal_pose",
+ "min_distance_scale": 0.4,
+ },
+ ),
+ "position_progress": RewardTermCfg(
+ func=target_mdp.position_progress_reward,
+ weight=1.75,
+ params={
+ "command_name": "goal_pose",
+ "position_tolerance": _TARGET_POSITION_TOLERANCE,
+ "yaw_tolerance": _TARGET_YAW_TOLERANCE,
+ "max_speed": 1.8,
+ },
+ ),
+ "near_goal_heading_alignment": RewardTermCfg(
+ func=target_mdp.near_goal_heading_alignment_reward,
+ weight=0.35,
+ params={
+ "command_name": "goal_pose",
+ "near_distance": 0.75,
+ },
+ ),
+ "near_goal_stability": RewardTermCfg(
+ func=target_mdp.near_goal_stability_reward,
+ weight=0.0,
+ params={
+ "command_name": "goal_pose",
+ "entity_name": "robot",
+ "near_distance": 0.60,
+ "lin_speed_std": 0.35,
+ "yaw_rate_std": 0.60,
+ "min_up_z": _TARGET_MIN_UP_Z,
+ },
+ ),
+ "yaw_progress": RewardTermCfg(
+ func=target_mdp.yaw_progress_reward,
+ weight=0.25,
+ params={
+ "command_name": "goal_pose",
+ "position_tolerance": _TARGET_POSITION_TOLERANCE,
+ "yaw_tolerance": _TARGET_YAW_TOLERANCE,
+ "near_distance": 0.75,
+ },
+ ),
+ "goal_position_bonus": RewardTermCfg(
+ func=target_mdp.goal_position_bonus,
+ weight=0.05,
+ params={
+ "command_name": "goal_pose",
+ "position_tolerance": _TARGET_POSITION_TOLERANCE,
+ "yaw_soft_tolerance": _TARGET_YAW_TOLERANCE,
+ },
+ ),
+ "goal_pose_bonus": RewardTermCfg(
+ func=target_mdp.goal_pose_terminal_bonus,
+ weight=0.2,
+ params={
+ "command_name": "goal_pose",
+ "position_tolerance": _TARGET_POSITION_TOLERANCE,
+ "yaw_tolerance": _TARGET_YAW_TOLERANCE,
+ "hold_steps": _TARGET_STABLE_HOLD_STEPS,
+ "entity_name": "robot",
+ "max_lin_speed": _TARGET_MAX_LIN_SPEED,
+ "max_yaw_rate": _TARGET_MAX_YAW_RATE,
+ "min_up_z": _TARGET_MIN_UP_Z,
+ },
+ ),
+ "goal_time_efficiency": RewardTermCfg(
+ func=target_mdp.goal_time_efficiency_bonus,
+ weight=0.0,
+ params={
+ "command_name": "goal_pose",
+ "position_tolerance": _TARGET_POSITION_TOLERANCE,
+ "yaw_tolerance": _TARGET_YAW_TOLERANCE,
+ "hold_steps": _TARGET_STABLE_HOLD_STEPS,
+ "max_speed": 1.8,
+ "entity_name": "robot",
+ "max_lin_speed": _TARGET_MAX_LIN_SPEED,
+ "max_yaw_rate": _TARGET_MAX_YAW_RATE,
+ "min_up_z": _TARGET_MIN_UP_Z,
+ },
+ ),
+ "fall_penalty": RewardTermCfg(
+ func=amp_mdp.fell_over_penalty,
+ weight=-0.1,
+ params={
+ "robot_name": "robot",
+ },
+ ),
+ }
+
+ terminations = {
+ "time_out": TerminationTermCfg(func=amp_mdp.time_out, time_out=True),
+ "fell_over": TerminationTermCfg(func=amp_mdp.fell_over),
+ }
+
+ cfg = ManagerBasedRlEnvCfg(
+ scene=SceneCfg(
+ terrain=TerrainEntityCfg(terrain_type="plane"),
+ num_envs=1,
+ extent=3.0,
+ ),
+ observations={
+ "actor": ObservationGroupCfg(
+ terms=actor_terms,
+ concatenate_terms=True,
+ enable_corruption=not play,
+ ),
+ "critic": ObservationGroupCfg(
+ terms=critic_terms,
+ concatenate_terms=True,
+ enable_corruption=False,
+ ),
+ },
+ actions=actions,
+ commands={
+ "goal_pose": target_mdp.GoalPoseCommandCfg(
+ entity_name="robot",
+ resampling_time_range=(6.0, 10.0),
+ debug_vis=play,
+ ranges=target_mdp.GoalPoseCommandCfg.Ranges(
+ target_radius=(0.4, 1.2),
+ target_yaw=(-0.25, 0.25),
+ target_angle=(-0.50, 0.50),
+ ),
+ )
+ },
+ events=events,
+ rewards=rewards,
+ terminations=terminations,
+ curriculum={},
+ metrics={
+ "fell_over_fraction": MetricsTermCfg(
+ func=amp_mdp.fell_over_fraction,
+ params={"robot_name": "robot"},
+ ),
+ },
+ viewer=ViewerConfig(
+ origin_type=ViewerConfig.OriginType.ASSET_BODY,
+ entity_name="robot",
+ body_name="torso",
+ distance=2.5,
+ elevation=-10.0,
+ azimuth=90.0,
+ width=640,
+ height=480,
+ ),
+ sim=SimulationCfg(
+ nconmax=80,
+ njmax=400,
+ mujoco=MujocoCfg(
+ timestep=0.005,
+ iterations=10,
+ ls_iterations=20,
+ ccd_iterations=80,
+ cone="elliptic",
+ impratio=5.0,
+ ),
+ ),
+ decimation=4,
+ episode_length_s=20.0,
+ )
+ cfg.scale_rewards_by_dt = False
+ if play:
+ goal_pose_cmd = cfg.commands["goal_pose"]
+ assert isinstance(goal_pose_cmd, target_mdp.GoalPoseCommandCfg)
+ goal_pose_cmd.ranges.target_radius = (0.4, 10.0)
+ goal_pose_cmd.ranges.target_yaw = (-math.pi, math.pi)
+ goal_pose_cmd.ranges.target_angle = (-math.pi, math.pi)
+ return cfg
diff --git a/src/mjlab_playground/tasks/tracking/__init__.py b/src/mjlab_playground/tasks/tracking/__init__.py
new file mode 100644
index 0000000..d5b1973
--- /dev/null
+++ b/src/mjlab_playground/tasks/tracking/__init__.py
@@ -0,0 +1 @@
+"""Tracking tasks for mjlab_playground."""
diff --git a/src/mjlab_playground/tasks/tracking/config/__init__.py b/src/mjlab_playground/tasks/tracking/config/__init__.py
new file mode 100644
index 0000000..e51a57b
--- /dev/null
+++ b/src/mjlab_playground/tasks/tracking/config/__init__.py
@@ -0,0 +1 @@
+"""Configuration modules for tracking tasks."""
diff --git a/src/mjlab_playground/tasks/tracking/config/t1/__init__.py b/src/mjlab_playground/tasks/tracking/config/t1/__init__.py
new file mode 100644
index 0000000..1b2c814
--- /dev/null
+++ b/src/mjlab_playground/tasks/tracking/config/t1/__init__.py
@@ -0,0 +1,13 @@
+from mjlab.tasks.registry import register_mjlab_task
+from mjlab.tasks.tracking.rl import MotionTrackingOnPolicyRunner
+
+from .env_cfgs import booster_t1_flat_tracking_env_cfg
+from .rl_cfg import booster_t1_tracking_ppo_runner_cfg
+
+register_mjlab_task(
+ task_id="Mjlab-Tracking-Flat-Booster-T1",
+ env_cfg=booster_t1_flat_tracking_env_cfg(),
+ play_env_cfg=booster_t1_flat_tracking_env_cfg(play=True),
+ rl_cfg=booster_t1_tracking_ppo_runner_cfg(),
+ runner_cls=MotionTrackingOnPolicyRunner,
+)
diff --git a/src/mjlab_playground/tasks/tracking/config/t1/env_cfgs.py b/src/mjlab_playground/tasks/tracking/config/t1/env_cfgs.py
new file mode 100644
index 0000000..c2d75c2
--- /dev/null
+++ b/src/mjlab_playground/tasks/tracking/config/t1/env_cfgs.py
@@ -0,0 +1,210 @@
+"""Booster T1 tracking environment configuration."""
+
+from __future__ import annotations
+
+import copy
+import math
+import os
+from pathlib import Path
+
+import torch
+from mjlab.entity import Entity
+from mjlab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg
+from mjlab.envs.mdp.actions import JointPositionActionCfg
+from mjlab.managers import EventTermCfg, TerminationTermCfg
+from mjlab.managers.observation_manager import ObservationGroupCfg
+from mjlab.sensor import ContactMatch, ContactSensorCfg
+from mjlab.tasks.tracking.mdp import MotionCommandCfg
+from mjlab.tasks.tracking.tracking_env_cfg import make_tracking_env_cfg
+
+from mjlab_playground.asset_zoo.robots.booster_t1.t1_constants import (
+ T1_ACTION_SCALE,
+ get_t1_robot_cfg,
+)
+from mjlab_playground.tasks.tracking.mdp.commands import (
+ TrackingMixedResetMotionCommandCfg,
+)
+from mjlab_playground.tasks.tracking.mdp.events import (
+ TRACKING_HOME_RESET_MASK_KEY,
+ sample_tracking_reset_source,
+)
+
+_MAX_ANG_VEL = 500 * math.pi / 180.0 # [rad/s]
+_HOME_RESET_PROBABILITY_TRAIN = 0.35
+_HOME_RESET_PROBABILITY_PLAY = 1.0
+_HOME_JOINT_VEL_RANGE = (0.0, 0.0)
+_HOME_HOLD_STEPS_TRAIN = 0
+_HOME_HOLD_STEPS_PLAY = 20
+
+_T1_TRACKING_BODY_NAMES = (
+ # MotionCommand reset treats body_names[0] as the floating base body.
+ "Trunk",
+ "Waist",
+ "Hip_Roll_Left",
+ "Shank_Left",
+ "left_foot_link",
+ "Hip_Roll_Right",
+ "Shank_Right",
+ "right_foot_link",
+ "AL1",
+ "AL3",
+ "left_hand_link",
+ "AR1",
+ "AR3",
+ "right_hand_link",
+)
+
+
+def _resolve_default_tracking_motion_file() -> str:
+ asset_motion_root = (
+ Path(__file__).resolve().parents[3]
+ / "asset_zoo"
+ / "motions"
+ / "retargeted"
+ / "tracking"
+ / "booster_t1_23dof"
+ )
+ candidates = [
+ os.getenv("MJLAB_TRACKING_MOTION_FILE", ""),
+ str(asset_motion_root / "0026_kicking2_stageii.booster_t1_23dof.motion.npz"),
+ ]
+ for candidate in candidates:
+ if candidate and Path(candidate).exists():
+ return candidate
+ return candidates[1]
+
+
+_TRACKING_MOTION_FILE = _resolve_default_tracking_motion_file()
+
+
+def base_ang_vel_exceed(
+ env: ManagerBasedRlEnv,
+ threshold: float,
+) -> torch.Tensor:
+ asset: Entity = env.scene["robot"]
+ ang_vel = asset.data.root_link_ang_vel_b
+ return torch.any(ang_vel.abs() > threshold, dim=-1)
+
+
+def booster_t1_flat_tracking_env_cfg(
+ has_state_estimation: bool = False,
+ play: bool = False,
+) -> ManagerBasedRlEnvCfg:
+ """Create Booster T1 flat tracking configuration."""
+ if _T1_TRACKING_BODY_NAMES[0] != "Trunk":
+ raise ValueError(
+ "Tracking body_names must start with 'Trunk' for Booster T1. "
+ "MotionCommand reset uses body_names[0] as root state."
+ )
+
+ cfg = make_tracking_env_cfg()
+ cfg.scene.entities = {"robot": get_t1_robot_cfg()}
+
+ self_collision_cfg = ContactSensorCfg(
+ name="self_collision",
+ primary=ContactMatch(mode="subtree", pattern="Trunk", entity="robot"),
+ secondary=ContactMatch(mode="subtree", pattern="Trunk", entity="robot"),
+ fields=("found", "force"),
+ reduce="none",
+ num_slots=1,
+ history_length=4,
+ )
+ cfg.scene.sensors = (self_collision_cfg,)
+
+ joint_pos_action = cfg.actions["joint_pos"]
+ assert isinstance(joint_pos_action, JointPositionActionCfg)
+ joint_pos_action.scale = T1_ACTION_SCALE
+
+ motion_cmd_base = cfg.commands["motion"]
+ assert isinstance(motion_cmd_base, MotionCommandCfg)
+ motion_cmd = TrackingMixedResetMotionCommandCfg(
+ entity_name=motion_cmd_base.entity_name,
+ resampling_time_range=motion_cmd_base.resampling_time_range,
+ debug_vis=motion_cmd_base.debug_vis,
+ pose_range=dict(motion_cmd_base.pose_range),
+ velocity_range=dict(motion_cmd_base.velocity_range),
+ joint_position_range=motion_cmd_base.joint_position_range,
+ adaptive_kernel_size=motion_cmd_base.adaptive_kernel_size,
+ adaptive_lambda=motion_cmd_base.adaptive_lambda,
+ adaptive_uniform_ratio=motion_cmd_base.adaptive_uniform_ratio,
+ adaptive_alpha=motion_cmd_base.adaptive_alpha,
+ sampling_mode=motion_cmd_base.sampling_mode,
+ motion_file=motion_cmd_base.motion_file,
+ anchor_body_name=motion_cmd_base.anchor_body_name,
+ body_names=motion_cmd_base.body_names,
+ viz=copy.deepcopy(motion_cmd_base.viz),
+ home_start_frame=0,
+ home_pose_range={},
+ home_velocity_range={},
+ home_joint_position_range=(0.0, 0.0),
+ home_joint_velocity_range=_HOME_JOINT_VEL_RANGE,
+ home_root_position_mode="motion_xyz",
+ home_root_orientation_mode="motion_yaw",
+ home_canonicalize_motion_yaw=False,
+ home_initial_hold_steps=_HOME_HOLD_STEPS_TRAIN,
+ )
+ cfg.commands["motion"] = motion_cmd
+ motion_cmd.motion_file = _TRACKING_MOTION_FILE
+ motion_cmd.anchor_body_name = "Trunk"
+ motion_cmd.body_names = _T1_TRACKING_BODY_NAMES
+
+ cfg.events["tracking_reset_source"] = EventTermCfg(
+ func=sample_tracking_reset_source,
+ mode="reset",
+ params={
+ "home_probability": _HOME_RESET_PROBABILITY_TRAIN,
+ "mask_key": TRACKING_HOME_RESET_MASK_KEY,
+ },
+ )
+
+ cfg.events["foot_friction"].params[
+ "asset_cfg"
+ ].geom_names = r"^(left|right)_foot[1-4]_collision$"
+ cfg.events["base_com"].params["asset_cfg"].body_names = ("Trunk",)
+
+ cfg.terminations["ee_body_pos"].params["body_names"] = (
+ "left_foot_link",
+ "right_foot_link",
+ "left_hand_link",
+ "right_hand_link",
+ )
+ cfg.terminations["base_ang_vel_exceed"] = TerminationTermCfg(
+ func=base_ang_vel_exceed,
+ params={"threshold": _MAX_ANG_VEL},
+ )
+ cfg.viewer.body_name = "Trunk"
+
+ # Remove state-estimation terms for pure proprio + motion command tracking.
+ if not has_state_estimation:
+ new_actor_terms = {
+ k: v
+ for k, v in cfg.observations["actor"].terms.items()
+ if k not in ["motion_anchor_pos_b", "base_lin_vel"]
+ }
+ cfg.observations["actor"] = ObservationGroupCfg(
+ terms=new_actor_terms,
+ concatenate_terms=True,
+ enable_corruption=True,
+ )
+
+ if play:
+ cfg.episode_length_s = int(1e9)
+ cfg.observations["actor"].enable_corruption = False
+ cfg.events.pop("push_robot", None)
+ cfg.events["tracking_reset_source"].params["home_probability"] = (
+ _HOME_RESET_PROBABILITY_PLAY
+ )
+
+ # Disable RSI randomization in play mode.
+ motion_cmd.pose_range = {}
+ motion_cmd.velocity_range = {}
+ motion_cmd.sampling_mode = "start"
+ # Match competition kickoff intuition: stand/home orientation, no forced
+ # leaning root pitch/roll from motion frame, and give a short warmup window
+ # before advancing the reference timeline.
+ motion_cmd.home_root_position_mode = "motion_xy_home_z"
+ motion_cmd.home_root_orientation_mode = "home"
+ motion_cmd.home_canonicalize_motion_yaw = True
+ motion_cmd.home_initial_hold_steps = _HOME_HOLD_STEPS_PLAY
+
+ return cfg
diff --git a/src/mjlab_playground/tasks/tracking/config/t1/rl_cfg.py b/src/mjlab_playground/tasks/tracking/config/t1/rl_cfg.py
new file mode 100644
index 0000000..cabf050
--- /dev/null
+++ b/src/mjlab_playground/tasks/tracking/config/t1/rl_cfg.py
@@ -0,0 +1,13 @@
+"""RL configuration for Booster T1 tracking."""
+
+from mjlab.rl import RslRlOnPolicyRunnerCfg
+from mjlab.tasks.tracking.config.g1.rl_cfg import unitree_g1_tracking_ppo_runner_cfg
+
+
+def booster_t1_tracking_ppo_runner_cfg() -> RslRlOnPolicyRunnerCfg:
+ """Create RL runner configuration for Booster T1 tracking task."""
+ cfg = unitree_g1_tracking_ppo_runner_cfg()
+ cfg.experiment_name = "t1_tracking"
+ cfg.run_name = "t1_tracking"
+ cfg.wandb_project = "mjlab_playground"
+ return cfg
diff --git a/src/mjlab_playground/tasks/tracking/mdp/__init__.py b/src/mjlab_playground/tasks/tracking/mdp/__init__.py
new file mode 100644
index 0000000..240bbad
--- /dev/null
+++ b/src/mjlab_playground/tasks/tracking/mdp/__init__.py
@@ -0,0 +1,4 @@
+from mjlab.tasks.tracking.mdp import * # noqa: F401, F403
+
+from .commands import * # noqa: F401, F403
+from .events import * # noqa: F401, F403
diff --git a/src/mjlab_playground/tasks/tracking/mdp/commands.py b/src/mjlab_playground/tasks/tracking/mdp/commands.py
new file mode 100644
index 0000000..a8aedb6
--- /dev/null
+++ b/src/mjlab_playground/tasks/tracking/mdp/commands.py
@@ -0,0 +1,329 @@
+"""Tracking command term extensions for mixed reset sources."""
+
+# pyright: reportIncompatibleVariableOverride=false, reportOptionalMemberAccess=false
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Literal
+
+import torch
+from mjlab.tasks.tracking.mdp.commands import MotionCommand, MotionCommandCfg
+from mjlab.utils.lab_api.math import (
+ quat_apply,
+ quat_from_euler_xyz,
+ quat_inv,
+ quat_mul,
+ sample_uniform,
+ yaw_quat,
+)
+
+from .events import TRACKING_HOME_RESET_MASK_KEY
+
+
+class TrackingMixedResetMotionCommand(MotionCommand):
+ """Motion command with optional home-pose reset on a subset of environments."""
+
+ cfg: "TrackingMixedResetMotionCommandCfg"
+ _hold_time_step_counter: torch.Tensor
+
+ def __init__(self, cfg: "TrackingMixedResetMotionCommandCfg", env):
+ super().__init__(cfg, env)
+ if self.cfg.home_canonicalize_motion_yaw:
+ self._canonicalize_motion_yaw_to_home()
+
+ def _canonicalize_motion_yaw_to_home(self) -> None:
+ """Rotate full motion trajectory so start-frame yaw matches home yaw.
+
+ This removes large global heading offsets in motion files while preserving
+ relative body kinematics and root pitch/roll profile.
+ """
+ default_root_state = self.robot.data.default_root_state
+ assert default_root_state is not None
+ max_frame = int(self.motion.time_step_total) - 1
+ start_frame = int(max(0, min(max_frame, self.cfg.home_start_frame)))
+
+ home_quat = default_root_state[0, 3:7].unsqueeze(0)
+ source_quat = self.motion.body_quat_w[start_frame, 0].unsqueeze(0)
+ home_yaw = yaw_quat(home_quat)
+ source_yaw = yaw_quat(source_quat)
+ delta = quat_mul(home_yaw, quat_inv(source_yaw))[0]
+
+ delta_quat = delta.view(1, 1, 4).expand(
+ self.motion.body_quat_w.shape[0], self.motion.body_quat_w.shape[1], 4
+ )
+ start_anchor_pos = self.motion.body_pos_w[start_frame, 0].view(1, 1, 3)
+
+ self.motion.body_pos_w = (
+ quat_apply(delta_quat, self.motion.body_pos_w - start_anchor_pos)
+ + start_anchor_pos
+ )
+ self.motion.body_quat_w = quat_mul(delta_quat, self.motion.body_quat_w)
+ self.motion.body_lin_vel_w = quat_apply(delta_quat, self.motion.body_lin_vel_w)
+ self.motion.body_ang_vel_w = quat_apply(delta_quat, self.motion.body_ang_vel_w)
+
+ def apply_gui_reset(self, env_ids: torch.Tensor) -> bool:
+ """Optionally keep base scrubber behavior for interactive frame jumps.
+
+ In play/debug for mixed-reset tracking we usually want viewer reset to preserve
+ the configured reset-source policy (home vs motion), so this defaults to False.
+ """
+ if self.cfg.gui_reset_to_frame:
+ return super().apply_gui_reset(env_ids)
+ return False
+
+ def _resample_command(self, env_ids: torch.Tensor):
+ if env_ids.numel() == 0:
+ return
+
+ # Guard against invalid indices and prefer CPU-side split logic to avoid
+ # opaque CUDA device-side assert when advanced indexing receives bad ids.
+ env_ids_cpu = env_ids.detach().to(device="cpu", dtype=torch.long)
+ min_id = int(env_ids_cpu.min().item())
+ max_id = int(env_ids_cpu.max().item())
+ if min_id < 0 or max_id >= self.num_envs:
+ raise RuntimeError(
+ f"Invalid env_ids in tracking resample: min={min_id}, max={max_id}, "
+ f"num_envs={self.num_envs}"
+ )
+
+ reset_mask = self._env.extras.get(self.cfg.home_reset_mask_key)
+ if (
+ isinstance(reset_mask, torch.Tensor)
+ and reset_mask.dtype == torch.bool
+ and reset_mask.shape == (self.num_envs,)
+ ):
+ reset_mask_cpu = reset_mask.detach().to(device="cpu")
+ home_select = reset_mask_cpu[env_ids_cpu]
+ home_env_ids = env_ids_cpu[home_select].to(device=env_ids.device)
+ motion_env_ids = env_ids_cpu[~home_select].to(device=env_ids.device)
+ else:
+ home_env_ids = env_ids.new_empty((0,), dtype=env_ids.dtype)
+ motion_env_ids = env_ids
+
+ if len(home_env_ids) > 0:
+ self._resample_home_command(home_env_ids)
+
+ # Important: MotionCommand._resample_command internally reads self.body_pos_w,
+ # which indexes with self.time_steps for all envs. Therefore, any env with an
+ # out-of-range timestep must be repaired before calling into the base method.
+ # Home envs are repaired above; now motion envs can be safely resampled.
+ if len(motion_env_ids) > 0:
+ super()._resample_command(motion_env_ids)
+
+ def _write_reference_state_to_sim(
+ self,
+ env_ids: torch.Tensor,
+ root_pos: torch.Tensor,
+ root_ori: torch.Tensor,
+ root_lin_vel: torch.Tensor,
+ root_ang_vel: torch.Tensor,
+ joint_pos: torch.Tensor,
+ joint_vel: torch.Tensor,
+ ) -> None:
+ """Write reference state with invariant or per-world joint limits."""
+ soft_limits = self.robot.data.soft_joint_pos_limits
+ assert soft_limits is not None
+ limits = (
+ soft_limits.expand(len(env_ids), -1, -1)
+ if soft_limits.shape[0] == 1
+ else soft_limits[env_ids]
+ )
+ joint_pos = torch.clip(joint_pos, limits[..., 0], limits[..., 1])
+ self.robot.write_joint_state_to_sim(joint_pos, joint_vel, env_ids=env_ids)
+
+ root_state = torch.cat([root_pos, root_ori, root_lin_vel, root_ang_vel], dim=-1)
+ self.robot.write_root_state_to_sim(root_state, env_ids=env_ids)
+ self.robot.reset(env_ids=env_ids)
+
+ def _resample_home_command(self, env_ids: torch.Tensor) -> None:
+ # Keep command time index fixed at the configured frame so the policy always
+ # starts from the same reference timing when using home reset.
+ max_frame = int(self.motion.time_step_total) - 1
+ start_frame = int(max(0, min(max_frame, self.cfg.home_start_frame)))
+ self.time_steps[env_ids] = start_frame
+
+ default_root_state = self.robot.data.default_root_state
+ default_joint_pos = self.robot.data.default_joint_pos
+ default_joint_vel = self.robot.data.default_joint_vel
+ assert default_root_state is not None
+ assert default_joint_pos is not None
+ assert default_joint_vel is not None
+
+ root_pos = default_root_state[env_ids, 0:3].clone()
+ root_ori = default_root_state[env_ids, 3:7].clone()
+ root_lin_vel = default_root_state[env_ids, 7:10].clone()
+ root_ang_vel = default_root_state[env_ids, 10:13].clone()
+
+ pose_ranges = torch.tensor(
+ [
+ self.cfg.home_pose_range.get(key, (0.0, 0.0))
+ for key in ["x", "y", "z", "roll", "pitch", "yaw"]
+ ],
+ device=self.device,
+ dtype=torch.float32,
+ )
+ pose_noise = sample_uniform(
+ pose_ranges[:, 0],
+ pose_ranges[:, 1],
+ (len(env_ids), 6),
+ device=self.device,
+ )
+ if self.cfg.home_root_position_mode == "motion_xyz":
+ reference_pos = (
+ self.motion.body_pos_w[start_frame, 0].unsqueeze(0).expand(len(env_ids), -1)
+ )
+ root_pos = reference_pos.clone()
+ elif self.cfg.home_root_position_mode == "motion_xy_home_z":
+ reference_pos = (
+ self.motion.body_pos_w[start_frame, 0].unsqueeze(0).expand(len(env_ids), -1)
+ )
+ root_pos[:, :2] = reference_pos[:, :2]
+ elif self.cfg.home_root_position_mode == "home":
+ pass
+ else:
+ raise ValueError(
+ f"Unsupported home_root_position_mode: {self.cfg.home_root_position_mode}"
+ )
+ root_pos += pose_noise[:, :3] + self._env.scene.env_origins[env_ids]
+ if self.cfg.home_root_orientation_mode == "motion":
+ reference_ori = (
+ self.motion.body_quat_w[start_frame, 0].unsqueeze(0).expand(len(env_ids), -1)
+ )
+ root_ori = reference_ori.clone()
+ elif self.cfg.home_root_orientation_mode == "motion_yaw":
+ reference_ori = (
+ self.motion.body_quat_w[start_frame, 0].unsqueeze(0).expand(len(env_ids), -1)
+ )
+ root_ori = yaw_quat(reference_ori)
+ elif self.cfg.home_root_orientation_mode == "home":
+ pass
+ else:
+ raise ValueError(
+ f"Unsupported home_root_orientation_mode: {self.cfg.home_root_orientation_mode}"
+ )
+ ori_delta = quat_from_euler_xyz(
+ pose_noise[:, 3],
+ pose_noise[:, 4],
+ pose_noise[:, 5],
+ )
+ root_ori = quat_mul(ori_delta, root_ori)
+
+ vel_ranges = torch.tensor(
+ [
+ self.cfg.home_velocity_range.get(key, (0.0, 0.0))
+ for key in ["x", "y", "z", "roll", "pitch", "yaw"]
+ ],
+ device=self.device,
+ dtype=torch.float32,
+ )
+ vel_noise = sample_uniform(
+ vel_ranges[:, 0],
+ vel_ranges[:, 1],
+ (len(env_ids), 6),
+ device=self.device,
+ )
+ root_lin_vel += vel_noise[:, :3]
+ root_ang_vel += vel_noise[:, 3:]
+
+ joint_pos = default_joint_pos[env_ids].clone()
+ joint_pos += sample_uniform(
+ lower=self.cfg.home_joint_position_range[0],
+ upper=self.cfg.home_joint_position_range[1],
+ size=joint_pos.shape,
+ device=joint_pos.device, # type: ignore[arg-type]
+ )
+
+ joint_vel = default_joint_vel[env_ids].clone()
+ joint_vel += sample_uniform(
+ lower=self.cfg.home_joint_velocity_range[0],
+ upper=self.cfg.home_joint_velocity_range[1],
+ size=joint_vel.shape,
+ device=joint_vel.device, # type: ignore[arg-type]
+ )
+
+ self._write_reference_state_to_sim(
+ env_ids,
+ root_pos,
+ root_ori,
+ root_lin_vel,
+ root_ang_vel,
+ joint_pos,
+ joint_vel,
+ )
+
+ # ManagerBasedRlEnv.reset() calls command_manager.compute(dt=0) once after
+ # resampling. Hold the just-reset home frame for that first compute, and
+ # optionally for extra warmup steps so policy can transition from stand/home.
+ hold_steps = int(max(0, self.cfg.home_initial_hold_steps))
+ if self.cfg.hold_home_frame_for_first_compute:
+ hold_steps += 1
+ if hold_steps > 0:
+ if not hasattr(self, "_hold_time_step_counter"):
+ self._hold_time_step_counter = torch.zeros(
+ self.num_envs, dtype=torch.long, device=self.device
+ )
+ self._hold_time_step_counter[env_ids] = hold_steps
+
+ # Mark home resets with deterministic sampling metrics.
+ self.metrics["sampling_entropy"][env_ids] = 0.0
+ self.metrics["sampling_top1_prob"][env_ids] = 1.0
+ self.metrics["sampling_top1_bin"][env_ids] = float(start_frame) / max(
+ int(self.bin_count), 1
+ )
+
+ def _update_command(self):
+ hold_counter = getattr(self, "_hold_time_step_counter", None)
+ if (
+ isinstance(hold_counter, torch.Tensor)
+ and hold_counter.dtype == torch.long
+ and hold_counter.shape == (self.num_envs,)
+ ):
+ increment_ids = torch.where(hold_counter <= 0)[0]
+ if increment_ids.numel() > 0:
+ self.time_steps[increment_ids] += 1
+ held_ids = torch.where(hold_counter > 0)[0]
+ if held_ids.numel() > 0:
+ hold_counter[held_ids] -= 1
+ else:
+ self.time_steps += 1
+
+ env_ids = torch.where(self.time_steps >= self.motion.time_step_total)[0]
+ if env_ids.numel() > 0:
+ self._resample_command(env_ids)
+
+ self.update_relative_body_poses()
+
+ if self.cfg.sampling_mode == "adaptive":
+ self.bin_failed_count = (
+ self.cfg.adaptive_alpha * self._current_bin_failed
+ + (1 - self.cfg.adaptive_alpha) * self.bin_failed_count
+ )
+ self._current_bin_failed.zero_()
+
+
+@dataclass(kw_only=True)
+class TrackingMixedResetMotionCommandCfg(MotionCommandCfg):
+ """Configuration for mixed motion/home reset in tracking."""
+
+ home_reset_mask_key: str = TRACKING_HOME_RESET_MASK_KEY
+ home_start_frame: int = 0
+ home_pose_range: dict[str, tuple[float, float]] | None = None
+ home_velocity_range: dict[str, tuple[float, float]] | None = None
+ home_joint_position_range: tuple[float, float] = (0.0, 0.0)
+ home_joint_velocity_range: tuple[float, float] = (0.0, 0.0)
+ home_root_position_mode: Literal["motion_xyz", "motion_xy_home_z", "home"] = (
+ "motion_xyz"
+ )
+ home_root_orientation_mode: Literal["motion", "motion_yaw", "home"] = "motion"
+ home_canonicalize_motion_yaw: bool = False
+ gui_reset_to_frame: bool = False
+ hold_home_frame_for_first_compute: bool = True
+ home_initial_hold_steps: int = 0
+
+ def build(self, env) -> TrackingMixedResetMotionCommand:
+ if self.home_pose_range is None:
+ self.home_pose_range = {}
+ if self.home_velocity_range is None:
+ self.home_velocity_range = {}
+ return TrackingMixedResetMotionCommand(self, env)
diff --git a/src/mjlab_playground/tasks/tracking/mdp/events.py b/src/mjlab_playground/tasks/tracking/mdp/events.py
new file mode 100644
index 0000000..4a7207e
--- /dev/null
+++ b/src/mjlab_playground/tasks/tracking/mdp/events.py
@@ -0,0 +1,47 @@
+"""Reset event helpers for tracking tasks."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+ from mjlab.envs import ManagerBasedRlEnv
+
+
+TRACKING_HOME_RESET_MASK_KEY = "tracking_home_reset_mask"
+
+
+def sample_tracking_reset_source(
+ env: ManagerBasedRlEnv,
+ env_ids: torch.Tensor | None,
+ home_probability: float,
+ mask_key: str = TRACKING_HOME_RESET_MASK_KEY,
+) -> None:
+ """Sample which envs should use the home-pose reset path.
+
+ This event does not write simulation state directly. It records a boolean mask in
+ ``env.extras``. The tracking motion command consumes this mask during command
+ resampling and applies either:
+ - the default motion-frame reset path, or
+ - the home-pose reset path.
+ """
+ if env_ids is None:
+ env_ids = torch.arange(env.num_envs, device=env.device, dtype=torch.int64)
+
+ p = float(max(0.0, min(1.0, home_probability)))
+
+ mask = env.extras.get(mask_key)
+ if not isinstance(mask, torch.Tensor) or mask.shape != (env.num_envs,):
+ mask = torch.zeros(env.num_envs, device=env.device, dtype=torch.bool)
+ env.extras[mask_key] = mask
+
+ if p <= 0.0:
+ mask[env_ids] = False
+ return
+ if p >= 1.0:
+ mask[env_ids] = True
+ return
+
+ mask[env_ids] = torch.rand(len(env_ids), device=env.device) < p
diff --git a/src/mjlab_playground/tasks/velocity/__init__.py b/src/mjlab_playground/tasks/velocity/__init__.py
new file mode 100644
index 0000000..d8c0c15
--- /dev/null
+++ b/src/mjlab_playground/tasks/velocity/__init__.py
@@ -0,0 +1,4 @@
+# Adapted from KaydenKnapik/BoosterT1mjlab (Apache-2.0); modified for this fork.
+"""Velocity tracking environments for legged robots."""
+
+from . import config as config
diff --git a/src/mjlab_playground/tasks/velocity/config/__init__.py b/src/mjlab_playground/tasks/velocity/config/__init__.py
new file mode 100644
index 0000000..a8552b1
--- /dev/null
+++ b/src/mjlab_playground/tasks/velocity/config/__init__.py
@@ -0,0 +1,2 @@
+# Adapted from KaydenKnapik/BoosterT1mjlab (Apache-2.0); modified for this fork.
+from . import t1 as t1
diff --git a/src/mjlab_playground/tasks/velocity/config/t1/__init__.py b/src/mjlab_playground/tasks/velocity/config/t1/__init__.py
new file mode 100644
index 0000000..ced7034
--- /dev/null
+++ b/src/mjlab_playground/tasks/velocity/config/t1/__init__.py
@@ -0,0 +1,14 @@
+# Adapted from KaydenKnapik/BoosterT1mjlab (Apache-2.0); modified for this fork.
+from mjlab.tasks.registry import register_mjlab_task
+from mjlab.tasks.velocity.rl import VelocityOnPolicyRunner
+
+from .env_cfgs import booster_t1_flat_env_cfg
+from .rl_cfg import booster_t1_ppo_runner_cfg
+
+register_mjlab_task(
+ task_id="Mjlab-Velocity-Flat-Booster-T1",
+ env_cfg=booster_t1_flat_env_cfg(),
+ play_env_cfg=booster_t1_flat_env_cfg(play=True),
+ rl_cfg=booster_t1_ppo_runner_cfg(),
+ runner_cls=VelocityOnPolicyRunner,
+)
diff --git a/src/mjlab_playground/tasks/velocity/config/t1/env_cfgs.py b/src/mjlab_playground/tasks/velocity/config/t1/env_cfgs.py
new file mode 100644
index 0000000..2debd17
--- /dev/null
+++ b/src/mjlab_playground/tasks/velocity/config/t1/env_cfgs.py
@@ -0,0 +1,300 @@
+# Adapted from KaydenKnapik/BoosterT1mjlab (Apache-2.0); modified for this fork.
+"""Booster T1 velocity environment configurations."""
+
+from mjlab.envs import ManagerBasedRlEnvCfg
+from mjlab.envs import mdp as envs_mdp
+from mjlab.envs.mdp.actions import JointPositionActionCfg
+from mjlab.managers.event_manager import EventTermCfg
+from mjlab.managers.reward_manager import RewardTermCfg
+from mjlab.sensor import (
+ ContactMatch,
+ ContactSensorCfg,
+ ObjRef,
+ RayCastSensorCfg,
+ RingPatternCfg,
+ TerrainHeightSensorCfg,
+)
+from mjlab.tasks.velocity import mdp
+
+from mjlab_playground.asset_zoo.robots.booster_t1.t1_constants import (
+ T1_ACTION_SCALE,
+ get_t1_robot_cfg,
+)
+from mjlab_playground.tasks.velocity.mdp import UniformVelocityCommandCfg
+from mjlab_playground.tasks.velocity.velocity_env_cfg import make_velocity_env_cfg
+
+
+def booster_t1_rough_env_cfg(play: bool = False) -> ManagerBasedRlEnvCfg:
+ """Create Booster T1 rough terrain velocity configuration."""
+ cfg = make_velocity_env_cfg()
+
+ cfg.sim.mujoco.ccd_iterations = 500
+ cfg.sim.contact_sensor_maxmatch = 500
+ cfg.sim.nconmax = 70
+
+ cfg.scene.entities = {"robot": get_t1_robot_cfg()}
+
+ # Set raycast sensor frame to T1 pelvis.
+ for sensor in cfg.scene.sensors or ():
+ if sensor.name == "terrain_scan":
+ assert isinstance(sensor, RayCastSensorCfg)
+ assert isinstance(sensor.frame, ObjRef)
+ sensor.frame.name = "Trunk"
+
+ site_names = ("left_foot", "right_foot")
+ geom_names = tuple(
+ f"{side}_foot{i}_collision" for side in ("left", "right") for i in range(1, 5)
+ )
+
+ # Wire foot height scan to per-foot sites.
+ for sensor in cfg.scene.sensors or ():
+ if sensor.name == "foot_height_scan":
+ assert isinstance(sensor, TerrainHeightSensorCfg)
+ sensor.frame = tuple(
+ ObjRef(type="site", name=s, entity="robot") for s in site_names
+ )
+ sensor.pattern = RingPatternCfg.single_ring(radius=0.03, num_samples=6)
+
+ feet_ground_cfg = ContactSensorCfg(
+ name="feet_ground_contact",
+ primary=ContactMatch(
+ mode="subtree",
+ pattern=r"^(left_foot_link|right_foot_link)$",
+ entity="robot",
+ ),
+ secondary=ContactMatch(mode="body", pattern="terrain"),
+ fields=("found", "force"),
+ reduce="netforce",
+ num_slots=1,
+ track_air_time=True,
+ )
+ self_collision_cfg = ContactSensorCfg(
+ name="self_collision",
+ primary=ContactMatch(mode="subtree", pattern="Trunk", entity="robot"),
+ secondary=ContactMatch(mode="subtree", pattern="Trunk", entity="robot"),
+ fields=("found", "force"),
+ reduce="none",
+ num_slots=1,
+ history_length=4,
+ )
+ cfg.scene.sensors = (cfg.scene.sensors or ()) + (
+ feet_ground_cfg,
+ self_collision_cfg,
+ )
+
+ if cfg.scene.terrain is not None and cfg.scene.terrain.terrain_generator is not None:
+ cfg.scene.terrain.terrain_generator.curriculum = True
+
+ joint_pos_action = cfg.actions["joint_pos"]
+ assert isinstance(joint_pos_action, JointPositionActionCfg)
+ joint_pos_action.scale = T1_ACTION_SCALE
+
+ cfg.viewer.body_name = "Trunk"
+
+ twist_cmd = cfg.commands["twist"]
+ assert isinstance(twist_cmd, UniformVelocityCommandCfg)
+ twist_cmd.viz.z_offset = 1.15
+
+ cfg.events["foot_friction"].params["asset_cfg"].geom_names = geom_names
+ cfg.events["base_com"].params["asset_cfg"].body_names = ("Trunk",)
+
+ # Rationale for std values:
+ # - Knees/hip_pitch get the loosest std to allow natural leg bending during stride.
+ # - Hip roll/yaw stay tighter to prevent excessive lateral sway and keep gait stable.
+ # - Ankle roll is very tight for balance; ankle pitch looser for foot clearance.
+ # - Waist roll/pitch stay tight to keep the torso upright and stable.
+ # - Shoulders/elbows get moderate freedom for natural arm swing during walking.
+ # - Wrists are loose (0.3) since they don't affect balance much.
+ # Running values are ~1.5-2x walking values to accommodate larger motion range.
+ cfg.rewards["pose"].params["std_standing"] = {".*": 0.05}
+ cfg.rewards["pose"].params["std_walking"] = {
+ # Head.
+ r"AAHead_yaw": 0.1,
+ r"Head_pitch": 0.1,
+ # Lower body.
+ r".*Hip_Pitch.*": 0.3,
+ r".*Hip_Roll.*": 0.15,
+ r".*Hip_Yaw.*": 0.15,
+ r".*Knee_Pitch.*": 0.35,
+ r".*Ankle_Pitch.*": 0.25,
+ r".*Ankle_Roll.*": 0.1,
+ # Waist.
+ r"Waist": 0.2,
+ # Arms.
+ r".*Shoulder_Pitch.*": 0.15,
+ r".*Shoulder_Roll.*": 0.15,
+ r".*Elbow_Pitch.*": 0.15,
+ r".*Elbow_Yaw.*": 0.1,
+ }
+ cfg.rewards["pose"].params["std_running"] = {
+ # Head.
+ r"AAHead_yaw": 0.15,
+ r"Head_pitch": 0.15,
+ # Lower body.
+ r".*Hip_Pitch.*": 0.5,
+ r".*Hip_Roll.*": 0.2,
+ r".*Hip_Yaw.*": 0.2,
+ r".*Knee_Pitch.*": 0.6,
+ r".*Ankle_Pitch.*": 0.35,
+ r".*Ankle_Roll.*": 0.15,
+ # Waist.
+ r"Waist": 0.3,
+ # Arms.
+ r".*Shoulder_Pitch.*": 0.5,
+ r".*Shoulder_Roll.*": 0.2,
+ r".*Elbow_Pitch.*": 0.35,
+ r".*Elbow_Yaw.*": 0.15,
+ }
+
+ cfg.rewards["upright"].params["asset_cfg"].body_names = ("Trunk",)
+ cfg.rewards["body_ang_vel"].params["asset_cfg"].body_names = ("Trunk",)
+
+ for reward_name in ["foot_clearance", "foot_slip"]:
+ cfg.rewards[reward_name].params["asset_cfg"].site_names = site_names
+
+ cfg.rewards["body_ang_vel"].weight = -0.05
+ cfg.rewards["angular_momentum"].weight = -0.05
+ # cfg.rewards["angular_momentum"].weight = -0.02
+ cfg.rewards["air_time"].weight = 0.02
+
+ cfg.rewards["self_collisions"] = RewardTermCfg(
+ func=mdp.self_collision_cost,
+ weight=-1.0,
+ params={"sensor_name": self_collision_cfg.name, "force_threshold": 10.0},
+ )
+
+ # Apply play mode overrides.
+ if play:
+ # Effectively infinite episode length.
+ cfg.episode_length_s = int(1e9)
+
+ cfg.observations["actor"].enable_corruption = False
+ cfg.events.pop("push_robot", None)
+ cfg.terminations.pop("out_of_terrain_bounds", None)
+ cfg.curriculum = {}
+ cfg.events["randomize_terrain"] = EventTermCfg(
+ func=envs_mdp.randomize_terrain,
+ mode="reset",
+ params={},
+ )
+
+ if cfg.scene.terrain is not None:
+ if cfg.scene.terrain.terrain_generator is not None:
+ cfg.scene.terrain.terrain_generator.curriculum = False
+ cfg.scene.terrain.terrain_generator.num_cols = 5
+ cfg.scene.terrain.terrain_generator.num_rows = 5
+ cfg.scene.terrain.terrain_generator.border_width = 10.0
+
+ return cfg
+
+
+def booster_t1_flat_env_cfg(play: bool = False) -> ManagerBasedRlEnvCfg:
+ """Create Booster T1 flat terrain velocity configuration."""
+ cfg = booster_t1_rough_env_cfg(play=play)
+
+ cfg.sim.njmax = 300
+ cfg.sim.mujoco.ccd_iterations = 50
+ cfg.sim.contact_sensor_maxmatch = 64
+ cfg.sim.nconmax = None
+
+ # Switch to flat terrain.
+ assert cfg.scene.terrain is not None
+ cfg.scene.terrain.terrain_type = "plane"
+ cfg.scene.terrain.terrain_generator = None
+
+ # Remove raycast sensor and height scan (no terrain to scan).
+ cfg.scene.sensors = tuple(
+ s for s in (cfg.scene.sensors or ()) if s.name != "terrain_scan"
+ )
+ del cfg.observations["actor"].terms["height_scan"]
+ del cfg.observations["critic"].terms["height_scan"]
+
+ cfg.terminations.pop("out_of_terrain_bounds", None)
+
+ # Disable terrain curriculum (not present in play mode since rough clears all).
+ cfg.curriculum.pop("terrain_levels", None)
+
+ if play:
+ twist_cmd = cfg.commands["twist"]
+ assert isinstance(twist_cmd, UniformVelocityCommandCfg)
+ # Resample visibly different commands during short play/video sessions.
+ twist_cmd.resampling_time_range = (1.0, 1.5)
+ twist_cmd.rel_standing_envs = 0.0
+ twist_cmd.rel_lateral_envs = 0.0
+ twist_cmd.rel_heading_envs = 1.0
+ twist_cmd.rel_forward_envs = 0.0
+ twist_cmd.ranges.lin_vel_x = (2.0, 3.0)
+ twist_cmd.ranges.lin_vel_y = (0.0, 0.0)
+ twist_cmd.ranges.ang_vel_z = (-0.7, 0.7)
+ twist_cmd.ranges.heading = (-0.5, 0.5)
+
+ return cfg
+
+
+def booster_t1_flat_velocity_yaw_env_cfg(play: bool = False) -> ManagerBasedRlEnvCfg:
+ """Create a flat x-plus-heading velocity task for small-command play tests."""
+ cfg = booster_t1_flat_env_cfg(play=play)
+
+ twist_cmd = cfg.commands["twist"]
+ assert isinstance(twist_cmd, UniformVelocityCommandCfg)
+ twist_cmd.rel_standing_envs = 0.1
+ twist_cmd.rel_lateral_envs = 0.0
+ twist_cmd.rel_heading_envs = 1.0
+ twist_cmd.rel_forward_envs = 0.0
+ twist_cmd.heading_command = True
+ twist_cmd.heading_control_stiffness = 0.75
+ twist_cmd.ranges.lin_vel_x = (-1.0, 1.5)
+ twist_cmd.ranges.lin_vel_y = (0.0, 0.0)
+ twist_cmd.ranges.ang_vel_z = (-0.7, 0.7)
+ twist_cmd.ranges.heading = (-0.7, 0.7)
+
+ return cfg
+
+
+def booster_t1_flat_velocity_raw_env_cfg(play: bool = False) -> ManagerBasedRlEnvCfg:
+ """Create a flat raw-command velocity task compatible with old 81-D checkpoints."""
+ cfg = booster_t1_flat_env_cfg(play=play)
+
+ cfg.observations["actor"].terms["command"].func = envs_mdp.generated_commands
+ cfg.observations["critic"].terms["command"].func = envs_mdp.generated_commands
+
+ cfg.rewards["track_angular_velocity"] = RewardTermCfg(
+ func=mdp.track_angular_velocity,
+ weight=2.0,
+ params={"command_name": "twist", "std": 0.7071067811865476},
+ )
+ cfg.rewards.pop("track_heading", None)
+
+ twist_cmd = cfg.commands["twist"]
+ assert isinstance(twist_cmd, UniformVelocityCommandCfg)
+ twist_cmd.rel_standing_envs = 0.1
+ twist_cmd.rel_lateral_envs = 0.0
+ twist_cmd.rel_heading_envs = 0.3
+ twist_cmd.rel_forward_envs = 0.2
+ twist_cmd.heading_command = True
+ twist_cmd.heading_control_stiffness = 0.5
+ twist_cmd.ranges.lin_vel_x = (-1.0, 1.0)
+ twist_cmd.ranges.lin_vel_y = (-1.0, 1.0)
+ twist_cmd.ranges.ang_vel_z = (-0.5, 0.5)
+ twist_cmd.ranges.heading = (-3.141592653589793, 3.141592653589793)
+
+ command_vel = cfg.curriculum.get("command_vel")
+ if command_vel is not None:
+ command_vel.params["velocity_stages"] = [
+ {
+ "step": 0,
+ "lin_vel_x": (-1.0, 1.0),
+ "lin_vel_y": (-1.0, 1.0),
+ "ang_vel_z": (-0.5, 0.5),
+ "heading": None,
+ },
+ {
+ "step": 120000,
+ "lin_vel_x": (-1.5, 1.5),
+ "lin_vel_y": (-1.0, 1.0),
+ "ang_vel_z": (-1.0, 1.0),
+ "heading": None,
+ },
+ ]
+
+ return cfg
diff --git a/src/mjlab_playground/tasks/velocity/config/t1/rl_cfg.py b/src/mjlab_playground/tasks/velocity/config/t1/rl_cfg.py
new file mode 100644
index 0000000..5835ec8
--- /dev/null
+++ b/src/mjlab_playground/tasks/velocity/config/t1/rl_cfg.py
@@ -0,0 +1,47 @@
+# Adapted from KaydenKnapik/BoosterT1mjlab (Apache-2.0); modified for this fork.
+"""RL configuration for Booster T1 velocity task."""
+
+from mjlab.rl import (
+ RslRlModelCfg,
+ RslRlOnPolicyRunnerCfg,
+ RslRlPpoAlgorithmCfg,
+)
+
+
+def booster_t1_ppo_runner_cfg() -> RslRlOnPolicyRunnerCfg:
+ """Create RL runner configuration for Booster T1 velocity task."""
+ return RslRlOnPolicyRunnerCfg(
+ actor=RslRlModelCfg(
+ hidden_dims=(512, 256, 128),
+ activation="elu",
+ obs_normalization=True,
+ distribution_cfg={
+ "class_name": "GaussianDistribution",
+ "init_std": 1.0,
+ "std_type": "scalar",
+ },
+ ),
+ critic=RslRlModelCfg(
+ hidden_dims=(512, 256, 128),
+ activation="elu",
+ obs_normalization=True,
+ ),
+ algorithm=RslRlPpoAlgorithmCfg(
+ value_loss_coef=1.0,
+ use_clipped_value_loss=True,
+ clip_param=0.2,
+ entropy_coef=0.01,
+ num_learning_epochs=5,
+ num_mini_batches=4,
+ learning_rate=1.0e-3,
+ schedule="adaptive",
+ gamma=0.99,
+ lam=0.95,
+ desired_kl=0.01,
+ max_grad_norm=1.0,
+ ),
+ experiment_name="t1_velocity",
+ save_interval=50,
+ num_steps_per_env=24,
+ max_iterations=30_000,
+ )
diff --git a/src/mjlab_playground/tasks/velocity/mdp/__init__.py b/src/mjlab_playground/tasks/velocity/mdp/__init__.py
new file mode 100644
index 0000000..969c830
--- /dev/null
+++ b/src/mjlab_playground/tasks/velocity/mdp/__init__.py
@@ -0,0 +1,8 @@
+# Adapted from KaydenKnapik/BoosterT1mjlab (Apache-2.0); modified for this fork.
+from mjlab.envs.mdp import * # noqa: F401, F403
+
+from .curriculums import * # noqa: F403
+from .observations import * # noqa: F403
+from .rewards import * # noqa: F403
+from .terminations import * # noqa: F403
+from .velocity_command import * # noqa: F403
diff --git a/src/mjlab_playground/tasks/velocity/mdp/curriculums.py b/src/mjlab_playground/tasks/velocity/mdp/curriculums.py
new file mode 100644
index 0000000..1ae1353
--- /dev/null
+++ b/src/mjlab_playground/tasks/velocity/mdp/curriculums.py
@@ -0,0 +1,114 @@
+# Adapted from KaydenKnapik/BoosterT1mjlab (Apache-2.0); modified for this fork.
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, TypedDict, cast
+
+import torch
+from mjlab.entity import Entity
+from mjlab.managers.scene_entity_config import SceneEntityCfg
+
+from .velocity_command import UniformVelocityCommandCfg
+
+if TYPE_CHECKING:
+ from mjlab.envs import ManagerBasedRlEnv
+
+_DEFAULT_SCENE_CFG = SceneEntityCfg("robot")
+
+
+class VelocityStage(TypedDict):
+ step: int
+ lin_vel_x: tuple[float, float] | None
+ lin_vel_y: tuple[float, float] | None
+ ang_vel_z: tuple[float, float] | None
+ heading: tuple[float, float] | None
+
+
+class RewardWeightStage(TypedDict):
+ step: int
+ weight: float
+
+
+def terrain_levels_vel(
+ env: ManagerBasedRlEnv,
+ env_ids: torch.Tensor,
+ command_name: str,
+ asset_cfg: SceneEntityCfg = _DEFAULT_SCENE_CFG,
+) -> torch.Tensor:
+ asset: Entity = env.scene[asset_cfg.name]
+
+ terrain = env.scene.terrain
+ assert terrain is not None
+ terrain_generator = terrain.cfg.terrain_generator
+ assert terrain_generator is not None
+
+ command = env.command_manager.get_command(command_name)
+ assert command is not None
+
+ # Compute the distance the robot walked.
+ distance = torch.norm(
+ asset.data.root_link_pos_w[env_ids, :2] - env.scene.env_origins[env_ids, :2], dim=1
+ )
+
+ # Robots that walked far enough progress to harder terrains.
+ move_up = distance > terrain_generator.size[0] / 2
+
+ # Robots that walked less than half of their required distance go to simpler
+ # terrains.
+ move_down = (
+ distance < torch.norm(command[env_ids, :2], dim=1) * env.max_episode_length_s * 0.5
+ )
+ move_down *= ~move_up
+
+ # Update terrain levels.
+ terrain.update_env_origins(env_ids, move_up, move_down)
+
+ return torch.mean(terrain.terrain_levels.float())
+
+
+def commands_vel(
+ env: ManagerBasedRlEnv,
+ env_ids: torch.Tensor,
+ command_name: str,
+ velocity_stages: list[VelocityStage],
+) -> dict[str, torch.Tensor]:
+ del env_ids # Unused.
+ command_term = env.command_manager.get_term(command_name)
+ assert command_term is not None
+ cfg = cast(UniformVelocityCommandCfg, command_term.cfg)
+ for stage in velocity_stages:
+ if env.common_step_counter > stage["step"]:
+ if "lin_vel_x" in stage and stage["lin_vel_x"] is not None:
+ cfg.ranges.lin_vel_x = stage["lin_vel_x"]
+ if "lin_vel_y" in stage and stage["lin_vel_y"] is not None:
+ cfg.ranges.lin_vel_y = stage["lin_vel_y"]
+ if "ang_vel_z" in stage and stage["ang_vel_z"] is not None:
+ cfg.ranges.ang_vel_z = stage["ang_vel_z"]
+ if "heading" in stage and stage["heading"] is not None:
+ cfg.ranges.heading = stage["heading"]
+ return {
+ "lin_vel_x_min": torch.tensor(cfg.ranges.lin_vel_x[0]),
+ "lin_vel_x_max": torch.tensor(cfg.ranges.lin_vel_x[1]),
+ "lin_vel_y_min": torch.tensor(cfg.ranges.lin_vel_y[0]),
+ "lin_vel_y_max": torch.tensor(cfg.ranges.lin_vel_y[1]),
+ "heading_min": torch.tensor(
+ cfg.ranges.heading[0] if cfg.ranges.heading is not None else 0.0
+ ),
+ "heading_max": torch.tensor(
+ cfg.ranges.heading[1] if cfg.ranges.heading is not None else 0.0
+ ),
+ }
+
+
+def reward_weight(
+ env: ManagerBasedRlEnv,
+ env_ids: torch.Tensor,
+ reward_name: str,
+ weight_stages: list[RewardWeightStage],
+) -> torch.Tensor:
+ """Update a reward term's weight based on training step stages."""
+ del env_ids # Unused.
+ reward_term_cfg = env.reward_manager.get_term_cfg(reward_name)
+ for stage in weight_stages:
+ if env.common_step_counter > stage["step"]:
+ reward_term_cfg.weight = stage["weight"]
+ return torch.tensor([reward_term_cfg.weight])
diff --git a/src/mjlab_playground/tasks/velocity/mdp/observations.py b/src/mjlab_playground/tasks/velocity/mdp/observations.py
new file mode 100644
index 0000000..d071d55
--- /dev/null
+++ b/src/mjlab_playground/tasks/velocity/mdp/observations.py
@@ -0,0 +1,86 @@
+# Adapted from KaydenKnapik/BoosterT1mjlab (Apache-2.0); modified for this fork.
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import torch
+from mjlab.entity import Entity
+from mjlab.managers.scene_entity_config import SceneEntityCfg
+from mjlab.sensor import ContactSensor
+from mjlab.utils.lab_api.math import wrap_to_pi
+
+if TYPE_CHECKING:
+ from mjlab.envs import ManagerBasedRlEnv
+
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+
+
+def generated_commands(env: ManagerBasedRlEnv, command_name: str) -> torch.Tensor:
+ """Expose command axes plus heading error encoding.
+
+ The velocity task mixes three mutually exclusive command modes: stand,
+ x+heading, and lateral-y without heading. Both linear command axes are
+ exposed directly and heading stays in sin/cos form for wraparound safety.
+ """
+ command = env.command_manager.get_command(command_name)
+ assert command is not None
+ command_term = env.command_manager.get_term(command_name)
+ heading_target = getattr(command_term, "heading_target", None)
+ assert heading_target is not None, (
+ f"Command term '{command_name}' does not expose a heading target. "
+ "Enable heading_command for this task."
+ )
+
+ robot = getattr(command_term, "robot", None)
+ if robot is None:
+ robot = env.scene["robot"]
+ heading_error = wrap_to_pi(heading_target - robot.data.heading_w)
+ is_heading_env = getattr(command_term, "is_heading_env", None)
+ if is_heading_env is not None:
+ heading_error = torch.where(
+ is_heading_env,
+ heading_error,
+ torch.zeros_like(heading_error),
+ )
+ is_standing_env = getattr(command_term, "is_standing_env", None)
+ if is_standing_env is not None:
+ heading_error = torch.where(
+ is_standing_env,
+ torch.zeros_like(heading_error),
+ heading_error,
+ )
+
+ return torch.stack(
+ (command[:, 0], command[:, 1], torch.sin(heading_error), torch.cos(heading_error)),
+ dim=1,
+ )
+
+
+def foot_height(
+ env: ManagerBasedRlEnv, asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG
+) -> torch.Tensor:
+ asset: Entity = env.scene[asset_cfg.name]
+ return asset.data.site_pos_w[:, asset_cfg.site_ids, 2] # (num_envs, num_sites)
+
+
+def foot_air_time(env: ManagerBasedRlEnv, sensor_name: str) -> torch.Tensor:
+ sensor: ContactSensor = env.scene[sensor_name]
+ sensor_data = sensor.data
+ current_air_time = sensor_data.current_air_time
+ assert current_air_time is not None
+ return current_air_time
+
+
+def foot_contact(env: ManagerBasedRlEnv, sensor_name: str) -> torch.Tensor:
+ sensor: ContactSensor = env.scene[sensor_name]
+ sensor_data = sensor.data
+ assert sensor_data.found is not None
+ return (sensor_data.found > 0).float()
+
+
+def foot_contact_forces(env: ManagerBasedRlEnv, sensor_name: str) -> torch.Tensor:
+ sensor: ContactSensor = env.scene[sensor_name]
+ sensor_data = sensor.data
+ assert sensor_data.force is not None
+ forces_flat = sensor_data.force.flatten(start_dim=1) # [B, N*3]
+ return torch.sign(forces_flat) * torch.log1p(torch.abs(forces_flat))
diff --git a/src/mjlab_playground/tasks/velocity/mdp/rewards.py b/src/mjlab_playground/tasks/velocity/mdp/rewards.py
new file mode 100644
index 0000000..66b956f
--- /dev/null
+++ b/src/mjlab_playground/tasks/velocity/mdp/rewards.py
@@ -0,0 +1,410 @@
+# Adapted from KaydenKnapik/BoosterT1mjlab (Apache-2.0); modified for this fork.
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import torch
+from mjlab.entity import Entity
+from mjlab.managers.reward_manager import RewardTermCfg
+from mjlab.managers.scene_entity_config import SceneEntityCfg
+from mjlab.sensor import BuiltinSensor, ContactSensor
+from mjlab.utils.lab_api.math import quat_apply_inverse, wrap_to_pi
+from mjlab.utils.lab_api.string import (
+ resolve_matching_names_values,
+)
+
+if TYPE_CHECKING:
+ from mjlab.envs import ManagerBasedRlEnv
+
+
+_DEFAULT_ASSET_CFG = SceneEntityCfg("robot")
+
+
+def _is_forward_command_active(
+ command: torch.Tensor, command_threshold: float
+) -> torch.Tensor:
+ """Gate rewards using only the x-command magnitude."""
+ return (torch.abs(command[:, 0]) > command_threshold).float()
+
+
+def track_linear_velocity(
+ env: ManagerBasedRlEnv,
+ std: float,
+ command_name: str,
+ asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> torch.Tensor:
+ """Reward for tracking the commanded base linear velocity.
+
+ The commanded z velocity is assumed to be zero.
+ """
+ asset: Entity = env.scene[asset_cfg.name]
+ command = env.command_manager.get_command(command_name)
+ assert command is not None, f"Command '{command_name}' not found."
+ actual = asset.data.root_link_lin_vel_b
+ xy_error = torch.sum(torch.square(command[:, :2] - actual[:, :2]), dim=1)
+ z_error = torch.square(actual[:, 2])
+ lin_vel_error = xy_error + z_error
+ return torch.exp(-lin_vel_error / std**2)
+
+
+def track_angular_velocity(
+ env: ManagerBasedRlEnv,
+ std: float,
+ command_name: str,
+ asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> torch.Tensor:
+ """Reward heading error for heading-controlled envs, angular velocity for others.
+
+ The commanded xy angular velocities are assumed to be zero.
+ """
+ asset: Entity = env.scene[asset_cfg.name]
+ command = env.command_manager.get_command(command_name)
+ assert command is not None, f"Command '{command_name}' not found."
+ actual = asset.data.root_link_ang_vel_b
+ z_error = torch.square(command[:, 2] - actual[:, 2])
+ xy_error = torch.sum(torch.square(actual[:, :2]), dim=1)
+ ang_vel_error = z_error + xy_error
+ return torch.exp(-ang_vel_error / std**2)
+
+
+def track_heading_command(
+ env: ManagerBasedRlEnv,
+ std: float,
+ command_name: str,
+ asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> torch.Tensor:
+ """Reward tracking of heading targets instead of yaw-rate commands."""
+ asset: Entity = env.scene[asset_cfg.name]
+ command_term = env.command_manager.get_term(command_name)
+ heading_target = getattr(command_term, "heading_target", None)
+ assert heading_target is not None, (
+ f"Command term '{command_name}' does not expose a heading target. "
+ "Enable heading_command for this task."
+ )
+
+ heading_error = wrap_to_pi(heading_target - asset.data.heading_w)
+ env.extras["log"]["Metrics/heading_error_mean"] = torch.mean(torch.abs(heading_error))
+ reward = torch.exp(-torch.square(heading_error) / std**2)
+ env.extras["log"]["Metrics/heading_reward_mean"] = torch.mean(reward)
+
+ active_mask = torch.ones_like(heading_error, dtype=torch.bool)
+ is_heading_env = getattr(command_term, "is_heading_env", None)
+ if is_heading_env is not None:
+ active_mask &= is_heading_env
+ is_standing_env = getattr(command_term, "is_standing_env", None)
+ if is_standing_env is not None:
+ active_mask &= ~is_standing_env
+
+ return torch.where(active_mask, reward, torch.ones_like(reward))
+
+
+def flat_orientation(
+ env: ManagerBasedRlEnv,
+ std: float,
+ asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> torch.Tensor:
+ """Reward flat base orientation (robot being upright).
+
+ If asset_cfg has body_ids specified, computes the projected gravity
+ for that specific body. Otherwise, uses the root link projected gravity.
+ """
+ asset: Entity = env.scene[asset_cfg.name]
+
+ # If body_ids are specified, compute projected gravity for that body.
+ if asset_cfg.body_ids:
+ body_quat_w = asset.data.body_link_quat_w[:, asset_cfg.body_ids, :] # [B, N, 4]
+ body_quat_w = body_quat_w.squeeze(1) # [B, 4]
+ gravity_w = asset.data.gravity_vec_w # [3]
+ projected_gravity_b = quat_apply_inverse(body_quat_w, gravity_w) # [B, 3]
+ xy_squared = torch.sum(torch.square(projected_gravity_b[:, :2]), dim=1)
+ else:
+ # Use root link projected gravity.
+ xy_squared = torch.sum(torch.square(asset.data.projected_gravity_b[:, :2]), dim=1)
+ return torch.exp(-xy_squared / std**2)
+
+
+def self_collision_cost(env: ManagerBasedRlEnv, sensor_name: str) -> torch.Tensor:
+ """Penalize self-collisions.
+
+ Returns the number of self-collisions detected by the specified contact sensor.
+ """
+ sensor: ContactSensor = env.scene[sensor_name]
+ assert sensor.data.found is not None
+ return sensor.data.found.squeeze(-1)
+
+
+def body_angular_velocity_penalty(
+ env: ManagerBasedRlEnv,
+ asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> torch.Tensor:
+ """Penalize excessive body angular velocities."""
+ asset: Entity = env.scene[asset_cfg.name]
+ ang_vel = asset.data.body_link_ang_vel_w[:, asset_cfg.body_ids, :]
+ ang_vel = ang_vel.squeeze(1)
+ ang_vel_xy = ang_vel[:, :2] # Don't penalize z-angular velocity.
+ return torch.sum(torch.square(ang_vel_xy), dim=1)
+
+
+def body_lateral_velocity_penalty(
+ env: ManagerBasedRlEnv,
+ asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> torch.Tensor:
+ """Penalize side-to-side base motion in the robot body frame."""
+ asset: Entity = env.scene[asset_cfg.name]
+ del asset_cfg
+ return torch.square(asset.data.root_link_lin_vel_b[:, 1])
+
+
+def angular_momentum_penalty(
+ env: ManagerBasedRlEnv,
+ sensor_name: str,
+) -> torch.Tensor:
+ """Penalize whole-body angular momentum to encourage natural arm swing."""
+ angmom_sensor: BuiltinSensor = env.scene[sensor_name]
+ angmom = angmom_sensor.data
+ angmom_magnitude_sq = torch.sum(torch.square(angmom), dim=-1)
+ angmom_magnitude = torch.sqrt(angmom_magnitude_sq)
+ env.extras["log"]["Metrics/angular_momentum_mean"] = torch.mean(angmom_magnitude)
+ return angmom_magnitude_sq
+
+
+def feet_air_time(
+ env: ManagerBasedRlEnv,
+ sensor_name: str,
+ threshold_min: float = 0.05,
+ threshold_max: float = 0.5,
+ command_name: str | None = None,
+ command_threshold: float = 0.5,
+) -> torch.Tensor:
+ """Reward feet air time."""
+ sensor: ContactSensor = env.scene[sensor_name]
+ sensor_data = sensor.data
+ current_air_time = sensor_data.current_air_time
+ assert current_air_time is not None
+ in_range = (current_air_time > threshold_min) & (current_air_time < threshold_max)
+ reward = torch.sum(in_range.float(), dim=1)
+ in_air = current_air_time > 0
+ num_in_air = torch.sum(in_air.float())
+ mean_air_time = torch.sum(current_air_time * in_air.float()) / torch.clamp(
+ num_in_air, min=1
+ )
+ env.extras["log"]["Metrics/air_time_mean"] = mean_air_time
+ if command_name is not None:
+ command = env.command_manager.get_command(command_name)
+ if command is not None:
+ linear_norm = torch.norm(command[:, :2], dim=1)
+ angular_norm = torch.abs(command[:, 2])
+ total_command = linear_norm + angular_norm
+ scale = (total_command > command_threshold).float()
+ reward *= scale
+ return reward
+
+
+def feet_clearance(
+ env: ManagerBasedRlEnv,
+ target_height: float,
+ command_name: str | None = None,
+ command_threshold: float = 0.01,
+ asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> torch.Tensor:
+ """Penalize deviation from target clearance height, weighted by foot velocity."""
+ asset: Entity = env.scene[asset_cfg.name]
+ foot_z = asset.data.site_pos_w[:, asset_cfg.site_ids, 2] # [B, N]
+ foot_vel_xy = asset.data.site_lin_vel_w[:, asset_cfg.site_ids, :2] # [B, N, 2]
+ vel_norm = torch.norm(foot_vel_xy, dim=-1) # [B, N]
+ delta = torch.abs(foot_z - target_height) # [B, N]
+ cost = torch.sum(delta * vel_norm, dim=1) # [B]
+ if command_name is not None:
+ command = env.command_manager.get_command(command_name)
+ if command is not None:
+ cost = cost * _is_forward_command_active(command, command_threshold)
+ return cost
+
+
+class feet_swing_height:
+ """Penalize deviation from target swing height, evaluated at landing."""
+
+ def __init__(self, cfg: RewardTermCfg, env: ManagerBasedRlEnv):
+ self.sensor_name = cfg.params["sensor_name"]
+ self.site_names = cfg.params["asset_cfg"].site_names
+ self.peak_heights = torch.zeros(
+ (env.num_envs, len(self.site_names)), device=env.device, dtype=torch.float32
+ )
+ self.step_dt = env.step_dt
+
+ def __call__(
+ self,
+ env: ManagerBasedRlEnv,
+ sensor_name: str,
+ target_height: float,
+ command_name: str,
+ command_threshold: float,
+ asset_cfg: SceneEntityCfg,
+ ) -> torch.Tensor:
+ asset: Entity = env.scene[asset_cfg.name]
+ contact_sensor: ContactSensor = env.scene[sensor_name]
+ command = env.command_manager.get_command(command_name)
+ assert command is not None
+ foot_heights = asset.data.site_pos_w[:, asset_cfg.site_ids, 2]
+ in_air = contact_sensor.data.found == 0
+ self.peak_heights = torch.where(
+ in_air,
+ torch.maximum(self.peak_heights, foot_heights),
+ self.peak_heights,
+ )
+ first_contact = contact_sensor.compute_first_contact(dt=self.step_dt)
+ active = _is_forward_command_active(command, command_threshold)
+ error = self.peak_heights / target_height - 1.0
+ cost = torch.sum(torch.square(error) * first_contact.float(), dim=1) * active
+ num_landings = torch.sum(first_contact.float())
+ peak_heights_at_landing = self.peak_heights * first_contact.float()
+ mean_peak_height = torch.sum(peak_heights_at_landing) / torch.clamp(
+ num_landings, min=1
+ )
+ env.extras["log"]["Metrics/peak_height_mean"] = mean_peak_height
+ self.peak_heights = torch.where(
+ first_contact,
+ torch.zeros_like(self.peak_heights),
+ self.peak_heights,
+ )
+ return cost
+
+
+def feet_slip(
+ env: ManagerBasedRlEnv,
+ sensor_name: str,
+ command_name: str,
+ command_threshold: float = 0.01,
+ asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG,
+) -> torch.Tensor:
+ """Penalize foot sliding (xy velocity while in contact)."""
+ asset: Entity = env.scene[asset_cfg.name]
+ contact_sensor: ContactSensor = env.scene[sensor_name]
+ command = env.command_manager.get_command(command_name)
+ assert command is not None
+ active = _is_forward_command_active(command, command_threshold)
+ assert contact_sensor.data.found is not None
+ in_contact = (contact_sensor.data.found > 0).float() # [B, N]
+ foot_vel_xy = asset.data.site_lin_vel_w[:, asset_cfg.site_ids, :2] # [B, N, 2]
+ vel_xy_norm = torch.norm(foot_vel_xy, dim=-1) # [B, N]
+ vel_xy_norm_sq = torch.square(vel_xy_norm) # [B, N]
+ cost = torch.sum(vel_xy_norm_sq * in_contact, dim=1) * active
+ num_in_contact = torch.sum(in_contact)
+ mean_slip_vel = torch.sum(vel_xy_norm * in_contact) / torch.clamp(
+ num_in_contact, min=1
+ )
+ env.extras["log"]["Metrics/slip_velocity_mean"] = mean_slip_vel
+ return cost
+
+
+def soft_landing(
+ env: ManagerBasedRlEnv,
+ sensor_name: str,
+ command_name: str | None = None,
+ command_threshold: float = 0.05,
+) -> torch.Tensor:
+ """Penalize high impact forces at landing to encourage soft footfalls."""
+ contact_sensor: ContactSensor = env.scene[sensor_name]
+ sensor_data = contact_sensor.data
+ assert sensor_data.force is not None
+ forces = sensor_data.force # [B, N, 3]
+ force_magnitude = torch.norm(forces, dim=-1) # [B, N]
+ first_contact = contact_sensor.compute_first_contact(dt=env.step_dt) # [B, N]
+ landing_impact = force_magnitude * first_contact.float() # [B, N]
+ cost = torch.sum(landing_impact, dim=1) # [B]
+ num_landings = torch.sum(first_contact.float())
+ mean_landing_force = torch.sum(landing_impact) / torch.clamp(num_landings, min=1)
+ env.extras["log"]["Metrics/landing_force_mean"] = mean_landing_force
+ if command_name is not None:
+ command = env.command_manager.get_command(command_name)
+ if command is not None:
+ linear_norm = torch.norm(command[:, :2], dim=1)
+ angular_norm = torch.abs(command[:, 2])
+ total_command = linear_norm + angular_norm
+ active = (total_command > command_threshold).float()
+ cost = cost * active
+ return cost
+
+
+class variable_posture:
+ """Penalize deviation from default pose with speed-dependent tolerance.
+
+ Uses per-joint standard deviations to control how much each joint can deviate
+ from default pose. Smaller std = stricter (less deviation allowed), larger
+ std = more forgiving. The reward is: exp(-mean(error² / std²))
+
+ Three speed regimes (based on linear + angular command velocity):
+ - std_standing (speed < walking_threshold): Tight tolerance for holding pose.
+ - std_walking (walking_threshold <= speed < running_threshold): Moderate.
+ - std_running (speed >= running_threshold): Loose tolerance for large motion.
+
+ Tune std values per joint based on how much motion that joint needs at each
+ speed. Map joint name patterns to std values, e.g. {".*knee.*": 0.35}.
+ """
+
+ def __init__(self, cfg: RewardTermCfg, env: ManagerBasedRlEnv):
+ asset: Entity = env.scene[cfg.params["asset_cfg"].name]
+ default_joint_pos = asset.data.default_joint_pos
+ assert default_joint_pos is not None
+ self.default_joint_pos = default_joint_pos
+
+ _, joint_names = asset.find_joints(cfg.params["asset_cfg"].joint_names)
+
+ _, _, std_standing = resolve_matching_names_values(
+ data=cfg.params["std_standing"],
+ list_of_strings=joint_names,
+ )
+ self.std_standing = torch.tensor(
+ std_standing, device=env.device, dtype=torch.float32
+ )
+
+ _, _, std_walking = resolve_matching_names_values(
+ data=cfg.params["std_walking"],
+ list_of_strings=joint_names,
+ )
+ self.std_walking = torch.tensor(std_walking, device=env.device, dtype=torch.float32)
+
+ _, _, std_running = resolve_matching_names_values(
+ data=cfg.params["std_running"],
+ list_of_strings=joint_names,
+ )
+ self.std_running = torch.tensor(std_running, device=env.device, dtype=torch.float32)
+
+ def __call__(
+ self,
+ env: ManagerBasedRlEnv,
+ std_standing,
+ std_walking,
+ std_running,
+ asset_cfg: SceneEntityCfg,
+ command_name: str,
+ walking_threshold: float = 0.5,
+ running_threshold: float = 1.5,
+ ) -> torch.Tensor:
+ del std_standing, std_walking, std_running # Unused.
+
+ asset: Entity = env.scene[asset_cfg.name]
+ command = env.command_manager.get_command(command_name)
+ assert command is not None
+
+ linear_speed = torch.norm(command[:, :2], dim=1)
+ angular_speed = torch.abs(command[:, 2])
+ total_speed = linear_speed + angular_speed
+
+ standing_mask = (total_speed < walking_threshold).float()
+ walking_mask = (
+ (total_speed >= walking_threshold) & (total_speed < running_threshold)
+ ).float()
+ running_mask = (total_speed >= running_threshold).float()
+
+ std = (
+ self.std_standing * standing_mask.unsqueeze(1)
+ + self.std_walking * walking_mask.unsqueeze(1)
+ + self.std_running * running_mask.unsqueeze(1)
+ )
+
+ current_joint_pos = asset.data.joint_pos[:, asset_cfg.joint_ids]
+ desired_joint_pos = self.default_joint_pos[:, asset_cfg.joint_ids]
+ error_squared = torch.square(current_joint_pos - desired_joint_pos)
+
+ return torch.exp(-torch.mean(error_squared / (std**2), dim=1))
diff --git a/src/mjlab_playground/tasks/velocity/mdp/terminations.py b/src/mjlab_playground/tasks/velocity/mdp/terminations.py
new file mode 100644
index 0000000..d8757c5
--- /dev/null
+++ b/src/mjlab_playground/tasks/velocity/mdp/terminations.py
@@ -0,0 +1,16 @@
+# Adapted from KaydenKnapik/BoosterT1mjlab (Apache-2.0); modified for this fork.
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import torch
+from mjlab.sensor import ContactSensor
+
+if TYPE_CHECKING:
+ from mjlab.envs import ManagerBasedRlEnv
+
+
+def illegal_contact(env: ManagerBasedRlEnv, sensor_name: str) -> torch.Tensor:
+ sensor: ContactSensor = env.scene[sensor_name]
+ assert sensor.data.found is not None
+ return torch.any(sensor.data.found, dim=-1)
diff --git a/src/mjlab_playground/tasks/velocity/mdp/velocity_command.py b/src/mjlab_playground/tasks/velocity/mdp/velocity_command.py
new file mode 100644
index 0000000..6f3afbf
--- /dev/null
+++ b/src/mjlab_playground/tasks/velocity/mdp/velocity_command.py
@@ -0,0 +1,393 @@
+# Adapted from KaydenKnapik/BoosterT1mjlab (Apache-2.0); modified for this fork.
+from __future__ import annotations
+
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+import torch
+from mjlab.entity import Entity
+from mjlab.managers.command_manager import CommandTerm, CommandTermCfg
+from mjlab.utils.lab_api.math import (
+ matrix_from_quat,
+ quat_apply,
+ wrap_to_pi,
+)
+
+if TYPE_CHECKING:
+ import viser
+ from mjlab.envs.manager_based_rl_env import ManagerBasedRlEnv
+ from mjlab.viewer.debug_visualizer import DebugVisualizer
+
+
+class UniformVelocityCommand(CommandTerm):
+ cfg: UniformVelocityCommandCfg
+
+ def __init__(self, cfg: UniformVelocityCommandCfg, env: ManagerBasedRlEnv):
+ super().__init__(cfg, env)
+
+ if self.cfg.heading_command and self.cfg.ranges.heading is None:
+ raise ValueError("heading_command=True but ranges.heading is set to None.")
+ if self.cfg.ranges.heading and not self.cfg.heading_command:
+ raise ValueError("ranges.heading is set but heading_command=False.")
+
+ self.robot: Entity = env.scene[cfg.entity_name]
+
+ self.vel_command_b = torch.zeros(self.num_envs, 3, device=self.device)
+ self.vel_command_w = torch.zeros(self.num_envs, 3, device=self.device)
+ self.heading_target = torch.zeros(self.num_envs, device=self.device)
+ self.heading_error = torch.zeros(self.num_envs, device=self.device)
+ self.is_heading_env = torch.zeros(
+ self.num_envs, dtype=torch.bool, device=self.device
+ )
+ self.is_standing_env = torch.zeros_like(self.is_heading_env)
+ self.is_lateral_env = torch.zeros_like(self.is_heading_env)
+ self.is_world_env = torch.zeros_like(self.is_heading_env)
+ self.is_forward_env = torch.zeros_like(self.is_heading_env)
+
+ self.metrics["error_vel_xy"] = torch.zeros(self.num_envs, device=self.device)
+ self.metrics["error_heading"] = torch.zeros(self.num_envs, device=self.device)
+ self._joystick_enabled: viser.GuiCheckboxHandle | None = None
+ self._joystick_sliders: list[viser.GuiSliderHandle] = []
+ self._joystick_get_env_idx: Callable[[], int] | None = None
+
+ @property
+ def command(self) -> torch.Tensor:
+ return self.vel_command_b
+
+ def _update_metrics(self) -> None:
+ max_command_time = self.cfg.resampling_time_range[1]
+ max_command_step = max_command_time / self._env.step_dt
+ self.metrics["error_vel_xy"] += (
+ torch.norm(
+ self.vel_command_b[:, :2] - self.robot.data.root_link_lin_vel_b[:, :2], dim=-1
+ )
+ / max_command_step
+ )
+ if self.cfg.heading_command:
+ self.metrics["error_heading"] += (
+ torch.abs(wrap_to_pi(self.heading_target - self.robot.data.heading_w))
+ / max_command_step
+ )
+
+ def _resample_command(self, env_ids: torch.Tensor) -> None:
+ # Modes are mutually exclusive:
+ # - stand: zero command
+ # - x + heading: x only, y = 0, heading active
+ # - lateral: y only, x = 0, heading inactive
+ num_envs = len(env_ids)
+ if num_envs == 0:
+ return
+
+ self.vel_command_b[env_ids] = 0.0
+ self.vel_command_w[env_ids] = 0.0
+ self.is_heading_env[env_ids] = False
+ self.is_standing_env[env_ids] = False
+ self.is_lateral_env[env_ids] = False
+ self.is_world_env[env_ids] = False
+ self.is_forward_env[env_ids] = False
+ self.heading_target[env_ids] = self.robot.data.heading_w[env_ids]
+
+ mode_sample = torch.rand(num_envs, device=self.device)
+ standing_mask = mode_sample < self.cfg.rel_standing_envs
+ lateral_mask = (mode_sample >= self.cfg.rel_standing_envs) & (
+ mode_sample < self.cfg.rel_standing_envs + self.cfg.rel_lateral_envs
+ )
+ x_heading_mask = ~(standing_mask | lateral_mask)
+
+ standing_env_ids = env_ids[standing_mask]
+ lateral_env_ids = env_ids[lateral_mask]
+ x_heading_env_ids = env_ids[x_heading_mask]
+
+ self.is_standing_env[standing_env_ids] = True
+ self.is_lateral_env[lateral_env_ids] = True
+ self.is_world_env[env_ids] = (
+ torch.rand(num_envs, device=self.device) <= self.cfg.rel_world_envs
+ )
+
+ if len(x_heading_env_ids) > 0:
+ self.vel_command_b[x_heading_env_ids, 0] = torch.empty(
+ len(x_heading_env_ids), device=self.device
+ ).uniform_(*self.cfg.ranges.lin_vel_x)
+ self.vel_command_b[x_heading_env_ids, 2] = torch.empty(
+ len(x_heading_env_ids), device=self.device
+ ).uniform_(*self.cfg.ranges.ang_vel_z)
+ if self.cfg.heading_command:
+ assert self.cfg.ranges.heading is not None
+ self.heading_target[x_heading_env_ids] = torch.empty(
+ len(x_heading_env_ids), device=self.device
+ ).uniform_(*self.cfg.ranges.heading)
+ self.is_heading_env[x_heading_env_ids] = (
+ torch.rand(len(x_heading_env_ids), device=self.device)
+ <= self.cfg.rel_heading_envs
+ )
+
+ if len(lateral_env_ids) > 0:
+ self.vel_command_b[lateral_env_ids, 1] = torch.empty(
+ len(lateral_env_ids), device=self.device
+ ).uniform_(*self.cfg.ranges.lin_vel_y)
+
+ self.vel_command_w[env_ids] = self.vel_command_b[env_ids]
+
+ fwd_candidates = x_heading_env_ids
+ if len(fwd_candidates) > 0:
+ self.is_forward_env[fwd_candidates] = (
+ torch.rand(len(fwd_candidates), device=self.device) <= self.cfg.rel_forward_envs
+ )
+ fwd_ids = fwd_candidates[self.is_forward_env[fwd_candidates]]
+ if len(fwd_ids) > 0:
+ self.vel_command_b[fwd_ids, 0] = (
+ self.vel_command_b[fwd_ids, 0].abs().clamp(min=0.3)
+ )
+ self.vel_command_b[fwd_ids, 1] = 0.0
+ self.vel_command_b[fwd_ids, 2] = 0.0
+ self.vel_command_w[fwd_ids] = self.vel_command_b[fwd_ids]
+
+ init_vel_env_ids = env_ids[
+ torch.rand(num_envs, device=self.device) < self.cfg.init_velocity_prob
+ ]
+ if len(init_vel_env_ids) > 0:
+ root_pos = self.robot.data.root_link_pos_w[init_vel_env_ids]
+ root_quat = self.robot.data.root_link_quat_w[init_vel_env_ids]
+ lin_vel_b = self.robot.data.root_link_lin_vel_b[init_vel_env_ids]
+ lin_vel_b[:, :2] = self.vel_command_b[init_vel_env_ids, :2]
+ root_lin_vel_w = quat_apply(root_quat, lin_vel_b)
+ root_ang_vel_b = self.robot.data.root_link_ang_vel_b[init_vel_env_ids]
+ root_ang_vel_b[:, 2] = self.vel_command_b[init_vel_env_ids, 2]
+ root_state = torch.cat(
+ [root_pos, root_quat, root_lin_vel_w, root_ang_vel_b], dim=-1
+ )
+ self.robot.write_root_state_to_sim(root_state, init_vel_env_ids)
+
+ def _update_command(self) -> None:
+ if self.cfg.heading_command:
+ self.heading_error = wrap_to_pi(self.heading_target - self.robot.data.heading_w)
+ env_ids = self.is_heading_env.nonzero(as_tuple=False).flatten()
+ self.vel_command_b[env_ids, 2] = torch.clip(
+ self.cfg.heading_control_stiffness * self.heading_error[env_ids],
+ min=self.cfg.ranges.ang_vel_z[0],
+ max=self.cfg.ranges.ang_vel_z[1],
+ )
+ if self.is_world_env.any():
+ w_ids = self.is_world_env.nonzero(as_tuple=False).flatten()
+ heading = self.robot.data.heading_w[w_ids]
+ cos_h = torch.cos(heading)
+ sin_h = torch.sin(heading)
+ vx_w = self.vel_command_w[w_ids, 0]
+ vy_w = self.vel_command_w[w_ids, 1]
+ self.vel_command_b[w_ids, 0] = cos_h * vx_w + sin_h * vy_w
+ self.vel_command_b[w_ids, 1] = -sin_h * vx_w + cos_h * vy_w
+ standing_env_ids = self.is_standing_env.nonzero(as_tuple=False).flatten()
+ self.vel_command_b[standing_env_ids, :] = 0.0
+ self.vel_command_w[standing_env_ids, :] = 0.0
+
+ # GUI.
+
+ def create_gui(
+ self,
+ name: str,
+ server: viser.ViserServer,
+ get_env_idx: Callable[[], int],
+ on_change: Callable[[], None] | None = None,
+ request_action: Callable[[str, Any], None] | None = None,
+ ) -> None:
+ """Create velocity controls for the Viser Commands panel."""
+ del on_change, request_action
+
+ from viser import Icon
+
+ ranges = self.cfg.ranges
+ axes = [
+ ("lin_vel_x", max(abs(v) for v in ranges.lin_vel_x)),
+ ("lin_vel_y", max(abs(v) for v in ranges.lin_vel_y)),
+ ]
+ if self.cfg.heading_command:
+ assert ranges.heading is not None
+ axes.append(("heading_error", max(abs(v) for v in ranges.heading)))
+ else:
+ axes.append(("ang_vel_z", max(abs(v) for v in ranges.ang_vel_z)))
+
+ sliders: list[viser.GuiSliderHandle] = []
+ with server.gui.add_folder(name.capitalize()):
+ enabled = server.gui.add_checkbox("Enable", initial_value=False)
+ for label, max_val in axes:
+ safe_max = max(max_val, 0.1)
+ max_input = server.gui.add_slider(
+ f"Max {label}",
+ initial_value=safe_max,
+ step=0.1,
+ min=0.1,
+ max=10.0,
+ )
+ slider = server.gui.add_slider(
+ label,
+ min=-safe_max,
+ max=safe_max,
+ step=0.05,
+ initial_value=0.0,
+ )
+
+ @max_input.on_update
+ def _(_ev, _slider=slider, _max_input=max_input) -> None:
+ _slider.min = -_max_input.value
+ _slider.max = _max_input.value
+
+ sliders.append(slider)
+
+ zero_btn = server.gui.add_button("Zero", icon=Icon.SQUARE_X)
+
+ @zero_btn.on_click
+ def _(_) -> None:
+ for slider in sliders:
+ slider.value = 0.0
+
+ self._joystick_enabled = enabled
+ self._joystick_sliders = sliders
+ self._joystick_get_env_idx = get_env_idx
+
+ def compute(self, dt: float) -> None:
+ super().compute(dt)
+ if self._joystick_enabled is None or not self._joystick_enabled.value:
+ return
+
+ assert self._joystick_get_env_idx is not None
+ idx = self._joystick_get_env_idx()
+ self.is_standing_env[idx] = False
+ self.is_lateral_env[idx] = False
+ self.is_world_env[idx] = False
+ self.is_forward_env[idx] = False
+ self.vel_command_b[idx, 0] = self._joystick_sliders[0].value
+ self.vel_command_b[idx, 1] = self._joystick_sliders[1].value
+ self.vel_command_w[idx, :2] = self.vel_command_b[idx, :2]
+
+ if self.cfg.heading_command:
+ heading_error = self._joystick_sliders[2].value
+ current_heading = self.robot.data.heading_w[idx]
+ self.is_heading_env[idx] = True
+ self.heading_error[idx] = heading_error
+ self.heading_target[idx] = wrap_to_pi(current_heading + heading_error)
+ self.vel_command_b[idx, 2] = torch.clamp(
+ self.cfg.heading_control_stiffness
+ * torch.tensor(heading_error, device=self.device),
+ min=self.cfg.ranges.ang_vel_z[0],
+ max=self.cfg.ranges.ang_vel_z[1],
+ )
+ else:
+ self.is_heading_env[idx] = False
+ self.vel_command_b[idx, 2] = self._joystick_sliders[2].value
+
+ # Visualization.
+
+ def _debug_vis_impl(self, visualizer: "DebugVisualizer") -> None:
+ """Draw velocity command and actual velocity arrows."""
+ env_indices = visualizer.get_env_indices(self.num_envs)
+ if not env_indices:
+ return
+
+ cmds = self.command.cpu().numpy()
+ base_pos_ws = self.robot.data.root_link_pos_w.cpu().numpy()
+ base_quat_w = self.robot.data.root_link_quat_w
+ base_mat_ws = matrix_from_quat(base_quat_w).cpu().numpy()
+ lin_vel_bs = self.robot.data.root_link_lin_vel_b.cpu().numpy()
+ ang_vel_bs = self.robot.data.root_link_ang_vel_b.cpu().numpy()
+
+ scale = self.cfg.viz.scale
+ z_offset = self.cfg.viz.z_offset
+
+ for batch in env_indices:
+ base_pos_w = base_pos_ws[batch]
+ base_mat_w = base_mat_ws[batch]
+ cmd = cmds[batch]
+ lin_vel_b = lin_vel_bs[batch]
+ ang_vel_b = ang_vel_bs[batch]
+
+ # Skip if robot appears uninitialized (at origin).
+ if np.linalg.norm(base_pos_w) < 1e-6:
+ continue
+
+ # Helper to transform local to world coordinates.
+ def local_to_world(
+ vec: np.ndarray, pos: np.ndarray = base_pos_w, mat: np.ndarray = base_mat_w
+ ) -> np.ndarray:
+ return pos + mat @ vec
+
+ # Command linear velocity arrow (blue).
+ cmd_lin_from = local_to_world(np.array([0, 0, z_offset]) * scale)
+ cmd_lin_to = local_to_world(
+ (np.array([0, 0, z_offset]) + np.array([cmd[0], cmd[1], 0])) * scale
+ )
+ visualizer.add_arrow(
+ cmd_lin_from, cmd_lin_to, color=(0.2, 0.2, 0.6, 0.6), width=0.015
+ )
+
+ # Command angular velocity arrow (green).
+ cmd_ang_from = cmd_lin_from
+ cmd_ang_to = local_to_world(
+ (np.array([0, 0, z_offset]) + np.array([0, 0, cmd[2]])) * scale
+ )
+ visualizer.add_arrow(
+ cmd_ang_from, cmd_ang_to, color=(0.2, 0.6, 0.2, 0.6), width=0.015
+ )
+
+ # Actual linear velocity arrow (cyan).
+ act_lin_from = local_to_world(np.array([0, 0, z_offset]) * scale)
+ act_lin_to = local_to_world(
+ (np.array([0, 0, z_offset]) + np.array([lin_vel_b[0], lin_vel_b[1], 0])) * scale
+ )
+ visualizer.add_arrow(
+ act_lin_from, act_lin_to, color=(0.0, 0.6, 1.0, 0.7), width=0.015
+ )
+
+ # Actual angular velocity arrow (light green).
+ act_ang_from = act_lin_from
+ act_ang_to = local_to_world(
+ (np.array([0, 0, z_offset]) + np.array([0, 0, ang_vel_b[2]])) * scale
+ )
+ visualizer.add_arrow(
+ act_ang_from, act_ang_to, color=(0.0, 1.0, 0.4, 0.7), width=0.015
+ )
+
+
+@dataclass(kw_only=True)
+class UniformVelocityCommandCfg(CommandTermCfg):
+ entity_name: str
+ heading_command: bool = False
+ heading_control_stiffness: float = 1.0
+ rel_standing_envs: float = 0.0
+ rel_lateral_envs: float = 0.0
+ rel_heading_envs: float = 1.0
+ rel_world_envs: float = 0.0
+ rel_forward_envs: float = 0.0
+ init_velocity_prob: float = 0.0
+
+ @dataclass
+ class Ranges:
+ lin_vel_x: tuple[float, float]
+ lin_vel_y: tuple[float, float]
+ ang_vel_z: tuple[float, float]
+ heading: tuple[float, float] | None = None
+
+ ranges: Ranges
+
+ @dataclass
+ class VizCfg:
+ z_offset: float = 0.2
+ scale: float = 0.5
+
+ viz: VizCfg = field(default_factory=VizCfg)
+
+ def build(self, env: ManagerBasedRlEnv) -> UniformVelocityCommand:
+ return UniformVelocityCommand(self, env)
+
+ def __post_init__(self):
+ if self.heading_command and self.ranges.heading is None:
+ raise ValueError(
+ "The velocity command has heading commands active (heading_command=True) but "
+ "the `ranges.heading` parameter is set to None."
+ )
+ mode_total = self.rel_standing_envs + self.rel_lateral_envs
+ if mode_total > 1.0:
+ raise ValueError(
+ "The sum of rel_standing_envs and rel_lateral_envs must be <= 1.0."
+ )
diff --git a/src/mjlab_playground/tasks/velocity/velocity_env_cfg.py b/src/mjlab_playground/tasks/velocity/velocity_env_cfg.py
new file mode 100644
index 0000000..62dbc13
--- /dev/null
+++ b/src/mjlab_playground/tasks/velocity/velocity_env_cfg.py
@@ -0,0 +1,477 @@
+# Adapted from KaydenKnapik/BoosterT1mjlab (Apache-2.0); modified for this fork.
+"""Velocity task configuration.
+
+This module provides a factory function to create a base velocity task config.
+Robot-specific configurations call the factory and customize as needed.
+"""
+
+import math
+from dataclasses import replace
+
+from mjlab.envs import ManagerBasedRlEnvCfg
+from mjlab.envs import mdp as envs_mdp
+from mjlab.envs.mdp import dr
+from mjlab.envs.mdp.actions import JointPositionActionCfg
+from mjlab.managers.action_manager import ActionTermCfg
+from mjlab.managers.command_manager import CommandTermCfg
+from mjlab.managers.curriculum_manager import CurriculumTermCfg
+from mjlab.managers.event_manager import EventTermCfg
+from mjlab.managers.metrics_manager import MetricsTermCfg
+from mjlab.managers.observation_manager import ObservationGroupCfg, ObservationTermCfg
+from mjlab.managers.reward_manager import RewardTermCfg
+from mjlab.managers.scene_entity_config import SceneEntityCfg
+from mjlab.managers.termination_manager import TerminationTermCfg
+from mjlab.scene import SceneCfg
+from mjlab.sensor import (
+ GridPatternCfg,
+ ObjRef,
+ RayCastSensorCfg,
+ TerrainHeightSensorCfg,
+)
+from mjlab.sim import MujocoCfg, SimulationCfg
+from mjlab.tasks.velocity import mdp
+from mjlab.terrains import TerrainEntityCfg
+from mjlab.terrains.config import ROUGH_TERRAINS_CFG
+from mjlab.utils.noise import UniformNoiseCfg as Unoise
+from mjlab.viewer import ViewerConfig
+
+from mjlab_playground.tasks.common import reset_joints_by_offset
+from mjlab_playground.tasks.velocity.mdp import UniformVelocityCommandCfg
+from mjlab_playground.tasks.velocity.mdp.curriculums import commands_vel
+from mjlab_playground.tasks.velocity.mdp.observations import (
+ generated_commands as heading_generated_commands,
+)
+from mjlab_playground.tasks.velocity.mdp.rewards import track_heading_command
+
+
+def make_velocity_env_cfg() -> ManagerBasedRlEnvCfg:
+ """Create base velocity tracking task configuration."""
+
+ ##
+ # Sensors
+ ##
+
+ terrain_scan = RayCastSensorCfg(
+ name="terrain_scan",
+ frame=ObjRef(type="body", name="", entity="robot"), # Set per-robot.
+ ray_alignment="yaw",
+ pattern=GridPatternCfg(size=(1.6, 1.0), resolution=0.1),
+ max_distance=5.0,
+ exclude_parent_body=True,
+ include_geom_groups=(0,), # Terrain only.
+ debug_vis=True,
+ )
+
+ foot_height_scan = TerrainHeightSensorCfg(
+ name="foot_height_scan",
+ frame=(), # Set per-robot: frame and pattern.
+ ray_alignment="yaw",
+ max_distance=1.0,
+ exclude_parent_body=True,
+ include_geom_groups=(0,), # Terrain only.
+ debug_vis=True,
+ viz=TerrainHeightSensorCfg.VizCfg(
+ show_rays=True,
+ hit_color=(1.0, 0.0, 1.0, 0.8), # Magenta rays.
+ hit_sphere_color=(1.0, 0.0, 1.0, 1.0),
+ ),
+ )
+
+ ##
+ # Observations
+ ##
+
+ actor_terms = {
+ "base_lin_vel": ObservationTermCfg(
+ func=mdp.builtin_sensor,
+ params={"sensor_name": "robot/imu_lin_vel"},
+ noise=Unoise(n_min=-0.5, n_max=0.5),
+ ),
+ "base_ang_vel": ObservationTermCfg(
+ func=mdp.builtin_sensor,
+ params={"sensor_name": "robot/imu_ang_vel"},
+ noise=Unoise(n_min=-0.2, n_max=0.2),
+ ),
+ "projected_gravity": ObservationTermCfg(
+ func=mdp.projected_gravity,
+ noise=Unoise(n_min=-0.05, n_max=0.05),
+ ),
+ "joint_pos": ObservationTermCfg(
+ func=mdp.joint_pos_rel,
+ noise=Unoise(n_min=-0.01, n_max=0.01),
+ ),
+ "joint_vel": ObservationTermCfg(
+ func=mdp.joint_vel_rel,
+ noise=Unoise(n_min=-1.5, n_max=1.5),
+ ),
+ "actions": ObservationTermCfg(func=mdp.last_action),
+ "command": ObservationTermCfg(
+ func=heading_generated_commands,
+ params={"command_name": "twist"},
+ ),
+ "height_scan": ObservationTermCfg(
+ func=envs_mdp.height_scan,
+ params={"sensor_name": "terrain_scan"},
+ noise=Unoise(n_min=-0.1, n_max=0.1),
+ scale=1 / terrain_scan.max_distance,
+ ),
+ }
+
+ critic_terms = {
+ **actor_terms,
+ "height_scan": ObservationTermCfg(
+ func=envs_mdp.height_scan,
+ params={"sensor_name": "terrain_scan"},
+ scale=1 / terrain_scan.max_distance,
+ ),
+ "foot_height": ObservationTermCfg(
+ func=mdp.foot_height,
+ params={"sensor_name": "foot_height_scan"},
+ ),
+ "foot_air_time": ObservationTermCfg(
+ func=mdp.foot_air_time,
+ params={"sensor_name": "feet_ground_contact"},
+ ),
+ "foot_contact": ObservationTermCfg(
+ func=mdp.foot_contact,
+ params={"sensor_name": "feet_ground_contact"},
+ ),
+ "foot_contact_forces": ObservationTermCfg(
+ func=mdp.foot_contact_forces,
+ params={"sensor_name": "feet_ground_contact"},
+ ),
+ }
+
+ observations = {
+ "actor": ObservationGroupCfg(
+ terms=actor_terms,
+ concatenate_terms=True,
+ enable_corruption=True,
+ ),
+ "critic": ObservationGroupCfg(
+ terms=critic_terms,
+ concatenate_terms=True,
+ enable_corruption=False,
+ ),
+ }
+
+ ##
+ # Metrics
+ ##
+
+ metrics = {
+ "mean_action_acc": MetricsTermCfg(
+ func=mdp.mean_action_acc,
+ ),
+ }
+
+ ##
+ # Actions
+ ##
+
+ actions: dict[str, ActionTermCfg] = {
+ "joint_pos": JointPositionActionCfg(
+ entity_name="robot",
+ actuator_names=(".*",),
+ scale=0.5, # Override per-robot.
+ use_default_offset=True,
+ )
+ }
+
+ ##
+ # Commands
+ ##
+
+ commands: dict[str, CommandTermCfg] = {
+ "twist": UniformVelocityCommandCfg(
+ entity_name="robot",
+ resampling_time_range=(3.0, 8.0),
+ rel_standing_envs=0.1,
+ rel_lateral_envs=0.2,
+ rel_heading_envs=1.0,
+ rel_forward_envs=0.0,
+ heading_command=True,
+ heading_control_stiffness=0.5,
+ debug_vis=True,
+ ranges=UniformVelocityCommandCfg.Ranges(
+ lin_vel_x=(-1.0, 1.0),
+ lin_vel_y=(-0.3, 0.3),
+ ang_vel_z=(-0.5, 0.5),
+ heading=(-math.pi, math.pi),
+ ),
+ )
+ }
+
+ ##
+ # Events
+ ##
+
+ events = {
+ "reset_base": EventTermCfg(
+ func=mdp.reset_root_state_uniform,
+ mode="reset",
+ params={
+ "pose_range": {
+ "x": (-0.5, 0.5),
+ "y": (-0.5, 0.5),
+ "z": (0.01, 0.05),
+ "yaw": (-3.14, 3.14),
+ },
+ "velocity_range": {},
+ },
+ ),
+ "reset_robot_joints": EventTermCfg(
+ func=reset_joints_by_offset,
+ mode="reset",
+ params={
+ "position_range": (0.0, 0.0),
+ "velocity_range": (0.0, 0.0),
+ "asset_cfg": SceneEntityCfg("robot", joint_names=(".*",)),
+ },
+ ),
+ "push_robot": EventTermCfg(
+ func=mdp.push_by_setting_velocity,
+ mode="interval",
+ interval_range_s=(1.0, 3.0),
+ params={
+ "velocity_range": {
+ "x": (-0.5, 0.5),
+ "y": (-0.5, 0.5),
+ "z": (-0.4, 0.4),
+ "roll": (-0.52, 0.52),
+ "pitch": (-0.52, 0.52),
+ "yaw": (-0.78, 0.78),
+ },
+ },
+ ),
+ "foot_friction": EventTermCfg(
+ mode="startup",
+ func=dr.geom_friction,
+ params={
+ "asset_cfg": SceneEntityCfg("robot", geom_names=()), # Set per-robot.
+ "operation": "abs",
+ "ranges": (0.3, 1.2),
+ "shared_random": True, # All foot geoms share the same friction.
+ },
+ ),
+ "encoder_bias": EventTermCfg(
+ mode="startup",
+ func=dr.encoder_bias,
+ params={
+ "asset_cfg": SceneEntityCfg("robot"),
+ "bias_range": (-0.015, 0.015),
+ },
+ ),
+ "base_com": EventTermCfg(
+ mode="startup",
+ func=dr.body_com_offset,
+ params={
+ "asset_cfg": SceneEntityCfg("robot", body_names=()), # Set per-robot.
+ "operation": "add",
+ "ranges": {
+ 0: (-0.025, 0.025),
+ 1: (-0.025, 0.025),
+ 2: (-0.03, 0.03),
+ },
+ },
+ ),
+ }
+
+ ##
+ # Rewards
+ ##
+
+ rewards = {
+ "track_linear_velocity": RewardTermCfg(
+ func=mdp.track_linear_velocity,
+ weight=2.0,
+ params={"command_name": "twist", "std": math.sqrt(0.25)},
+ ),
+ "track_heading": RewardTermCfg(
+ func=track_heading_command,
+ weight=2.0,
+ params={"command_name": "twist", "std": math.sqrt(0.5)},
+ ),
+ "upright": RewardTermCfg(
+ func=mdp.upright,
+ weight=1.0,
+ params={
+ "std": math.sqrt(0.2),
+ "asset_cfg": SceneEntityCfg("robot", body_names=()), # Set per-robot.
+ },
+ ),
+ "pose": RewardTermCfg(
+ func=mdp.variable_posture,
+ weight=1.0,
+ params={
+ "asset_cfg": SceneEntityCfg("robot", joint_names=(".*",)),
+ "command_name": "twist",
+ "std_standing": {}, # Set per-robot.
+ "std_walking": {}, # Set per-robot.
+ "std_running": {}, # Set per-robot.
+ "walking_threshold": 0.05,
+ "running_threshold": 1.5,
+ },
+ ),
+ "body_ang_vel": RewardTermCfg(
+ func=mdp.body_angular_velocity_penalty,
+ weight=0.0, # Override per-robot
+ params={"asset_cfg": SceneEntityCfg("robot", body_names=())}, # Set per-robot.
+ ),
+ "angular_momentum": RewardTermCfg(
+ func=mdp.angular_momentum_penalty,
+ weight=0.0, # Override per-robot
+ params={"sensor_name": "robot/root_angmom"},
+ ),
+ "dof_pos_limits": RewardTermCfg(func=mdp.joint_pos_limits, weight=-1.0),
+ "action_rate_l2": RewardTermCfg(func=mdp.action_rate_l2, weight=-0.1),
+ "air_time": RewardTermCfg(
+ func=mdp.feet_air_time,
+ weight=0.0, # Override per-robot.
+ params={
+ "sensor_name": "feet_ground_contact",
+ "threshold_min": 0.05,
+ "threshold_max": 0.5,
+ "command_name": "twist",
+ "command_threshold": 0.5,
+ },
+ ),
+ "foot_clearance": RewardTermCfg(
+ func=mdp.feet_clearance,
+ weight=-2.0,
+ params={
+ "target_height": 0.1,
+ "height_sensor_name": "foot_height_scan",
+ "command_name": "twist",
+ "command_threshold": 0.05,
+ "asset_cfg": SceneEntityCfg("robot", site_names=()), # Set per-robot.
+ },
+ ),
+ "foot_swing_height": RewardTermCfg(
+ func=mdp.feet_swing_height,
+ weight=-0.25,
+ params={
+ "sensor_name": "feet_ground_contact",
+ "height_sensor_name": "foot_height_scan",
+ "target_height": 0.1,
+ "command_name": "twist",
+ "command_threshold": 0.05,
+ },
+ ),
+ "foot_slip": RewardTermCfg(
+ func=mdp.feet_slip,
+ weight=-0.1,
+ params={
+ "sensor_name": "feet_ground_contact",
+ "command_name": "twist",
+ "command_threshold": 0.05,
+ "asset_cfg": SceneEntityCfg("robot", site_names=()), # Set per-robot.
+ },
+ ),
+ "soft_landing": RewardTermCfg(
+ func=mdp.soft_landing,
+ weight=-1e-5,
+ params={
+ "sensor_name": "feet_ground_contact",
+ "command_name": "twist",
+ "command_threshold": 0.05,
+ },
+ ),
+ }
+
+ ##
+ # Terminations
+ ##
+
+ terminations = {
+ "time_out": TerminationTermCfg(func=mdp.time_out, time_out=True),
+ "fell_over": TerminationTermCfg(
+ func=mdp.bad_orientation,
+ params={"limit_angle": math.radians(70.0)},
+ ),
+ "out_of_terrain_bounds": TerminationTermCfg(
+ func=mdp.out_of_terrain_bounds,
+ time_out=True,
+ ),
+ }
+
+ ##
+ # Curriculum
+ ##
+
+ curriculum = {
+ "terrain_levels": CurriculumTermCfg(
+ func=mdp.terrain_levels_vel,
+ params={"command_name": "twist"},
+ ),
+ "command_vel": CurriculumTermCfg(
+ func=commands_vel,
+ params={
+ "command_name": "twist",
+ "velocity_stages": [
+ {
+ "step": 0,
+ "lin_vel_x": (-0.5, 1.5),
+ "lin_vel_y": (-0.3, 0.3),
+ "heading": (-0.5, 0.5),
+ },
+ {
+ "step": 2500 * 24,
+ "lin_vel_x": (-1.0, 2.5),
+ "lin_vel_y": (-0.6, 0.6),
+ "heading": (-0.7, 0.7),
+ },
+ {
+ "step": 5000 * 24,
+ "lin_vel_x": (0.0, 3.0),
+ "lin_vel_y": (-1.0, 1.0),
+ "heading": (-math.pi * 0.5, math.pi * 0.5),
+ },
+ ],
+ },
+ ),
+ }
+
+ ##
+ # Assemble and return
+ ##
+
+ return ManagerBasedRlEnvCfg(
+ scene=SceneCfg(
+ terrain=TerrainEntityCfg(
+ terrain_type="generator",
+ terrain_generator=replace(ROUGH_TERRAINS_CFG),
+ max_init_terrain_level=5,
+ ),
+ sensors=(terrain_scan, foot_height_scan),
+ num_envs=1,
+ extent=2.0,
+ ),
+ observations=observations,
+ actions=actions,
+ commands=commands,
+ events=events,
+ rewards=rewards,
+ terminations=terminations,
+ curriculum=curriculum,
+ metrics=metrics,
+ viewer=ViewerConfig(
+ origin_type=ViewerConfig.OriginType.ASSET_BODY,
+ entity_name="robot",
+ body_name="", # Set per-robot.
+ distance=3.0,
+ elevation=-5.0,
+ azimuth=90.0,
+ ),
+ sim=SimulationCfg(
+ nconmax=35,
+ njmax=1500,
+ mujoco=MujocoCfg(
+ timestep=0.005,
+ iterations=10,
+ ls_iterations=20,
+ ),
+ ),
+ decimation=4,
+ episode_length_s=20.0,
+ )
diff --git a/tests/test_getup.py b/tests/test_getup.py
index faa3d40..334b2f2 100644
--- a/tests/test_getup.py
+++ b/tests/test_getup.py
@@ -8,8 +8,9 @@
from mjlab.envs import ManagerBasedRlEnv
from mjlab.managers.scene_entity_config import SceneEntityCfg
from mjlab.scene import Scene
-from mjlab_playground.getup.config.go1.env_cfgs import unitree_go1_getup_env_cfg
-from mjlab_playground.getup.config.t1.env_cfgs import booster_t1_getup_env_cfg
+from mjlab_playground.tasks.getup.config.go1.env_cfgs import unitree_go1_getup_env_cfg
+from mjlab_playground.tasks.getup.config.k1.env_cfgs import booster_k1_getup_env_cfg
+from mjlab_playground.tasks.getup.config.t1.env_cfgs import booster_t1_getup_env_cfg
_NUM_ENVS = 5
@@ -65,6 +66,14 @@ def go1_model() -> mujoco.MjModel:
return scene.compile()
+@pytest.fixture(scope="module")
+def k1_model() -> mujoco.MjModel:
+ cfg = booster_k1_getup_env_cfg()
+ cfg.scene.num_envs = 1
+ scene = Scene(cfg.scene, "cpu")
+ return scene.compile()
+
+
@pytest.fixture(scope="module")
def t1_env() -> ManagerBasedRlEnv:
cfg = booster_t1_getup_env_cfg()
@@ -83,6 +92,15 @@ def go1_env() -> ManagerBasedRlEnv:
return env
+@pytest.fixture(scope="module")
+def k1_env() -> ManagerBasedRlEnv:
+ cfg = booster_k1_getup_env_cfg()
+ cfg.scene.num_envs = _NUM_ENVS
+ env = ManagerBasedRlEnv(cfg, device="cpu")
+ env.reset(seed=0)
+ return env
+
+
# Collision priority.
@@ -100,6 +118,10 @@ def test_go1_collision_priority(go1_model: mujoco.MjModel) -> None:
assert priority == 1, f"Go1 geom {name!r} has priority={priority}, expected 1"
+def test_k1_actuator_count(k1_model: mujoco.MjModel) -> None:
+ assert k1_model.nu == 22
+
+
# Friction domain randomization.
@@ -127,3 +149,21 @@ def test_go1_friction_dr(go1_env: ManagerBasedRlEnv) -> None:
_assert_varied_across_worlds(
foot_friction[:, :, 2], "Go1 foot roll friction (axis 2)"
)
+
+
+def test_k1_getup_smoke_and_obs_shapes(k1_env: ManagerBasedRlEnv) -> None:
+ obs, _ = k1_env.reset(seed=0)
+
+ assert obs["actor"].shape[-1] == 72
+ assert obs["critic"].shape[-1] == 75
+ assert torch.isfinite(obs["actor"]).all()
+ assert torch.isfinite(obs["critic"]).all()
+ assert k1_env.action_manager.action.shape[-1] == 22
+
+ action = torch.zeros_like(k1_env.action_manager.action)
+ obs, reward, terminated, truncated, _ = k1_env.step(action)
+ assert torch.isfinite(obs["actor"]).all()
+ assert torch.isfinite(obs["critic"]).all()
+ assert torch.isfinite(reward).all()
+ assert terminated.shape[0] == _NUM_ENVS
+ assert truncated.shape[0] == _NUM_ENVS
diff --git a/tests/test_target_location_amp_t1.py b/tests/test_target_location_amp_t1.py
new file mode 100644
index 0000000..77c9e8c
--- /dev/null
+++ b/tests/test_target_location_amp_t1.py
@@ -0,0 +1,359 @@
+"""Tests for T1 target-location AMP task variants."""
+
+from __future__ import annotations
+
+import math
+import os
+from pathlib import Path
+from types import SimpleNamespace
+
+import mjlab_playground # noqa: F401
+import mujoco
+import pytest
+import torch
+from mjlab.tasks.registry import list_tasks, load_env_cfg, load_rl_cfg
+from mjlab_playground.tasks.target_location_amp.mdp.commands import GoalPoseCommand
+from mjlab_playground.tasks.target_location_amp.mdp.state import (
+ update_goal_pose_success_hold_state,
+)
+
+COMPETITION_FOOT_TASK_ID = "Mjlab-TargetLocationAmp-CompetitionFoot-Flat-Booster-T1"
+COMPETITION_COLLISION_TASK_ID = (
+ "Mjlab-TargetLocationAmp-CompetitionCollision-Flat-Booster-T1"
+)
+COMPETITION_T1_XML = Path(os.getenv("MJLAB_COMPETITION_T1_XML", ""))
+
+
+def test_t1_target_location_amp_competition_foot_task_registered() -> None:
+ assert COMPETITION_FOOT_TASK_ID in list_tasks()
+ assert COMPETITION_COLLISION_TASK_ID in list_tasks()
+
+
+def test_t1_target_location_amp_competition_foot_uses_box_contacts() -> None:
+ cfg = load_env_cfg(COMPETITION_FOOT_TASK_ID)
+ play_cfg = load_env_cfg(COMPETITION_FOOT_TASK_ID, play=True)
+ rl_cfg = load_rl_cfg(COMPETITION_FOOT_TASK_ID)
+
+ robot_cfg = cfg.scene.entities["robot"]
+ spec = robot_cfg.spec_fn()
+ foot_geoms = {geom.name: geom for geom in spec.geoms if "foot" in geom.name}
+
+ assert "left_foot" in foot_geoms
+ assert "right_foot" in foot_geoms
+ assert foot_geoms["left_foot"].type == mujoco.mjtGeom.mjGEOM_BOX
+ assert foot_geoms["right_foot"].type == mujoco.mjtGeom.mjGEOM_BOX
+ assert foot_geoms["left_foot"].condim == 3
+ assert foot_geoms["right_foot"].condim == 3
+ assert tuple(foot_geoms["left_foot"].friction) == (1.0, 0.01, 0.005)
+ assert tuple(foot_geoms["right_foot"].friction) == (1.0, 0.01, 0.005)
+ assert tuple(foot_geoms["left_foot"].solref) == (0.02, 1.0)
+ assert tuple(foot_geoms["right_foot"].solref) == (0.02, 1.0)
+ assert tuple(foot_geoms["left_foot"].solimp) == (0.015, 1.0, 0.015, 0.5, 2.0)
+ assert tuple(foot_geoms["right_foot"].solimp) == (0.015, 1.0, 0.015, 0.5, 2.0)
+ assert not any(name.endswith("_collision") for name in foot_geoms)
+
+ foot_friction_cfg = cfg.events["foot_friction"].params["asset_cfg"]
+ assert foot_friction_cfg.geom_names == r"^(left|right)_foot$"
+ assert "foot_friction" not in play_cfg.events
+ assert tuple(cfg.observations["actor"].terms) == (
+ "base_ang_vel",
+ "projected_gravity",
+ "joint_pos",
+ "joint_vel",
+ "actions",
+ "goal_local_pos",
+ "heading_to_target",
+ "final_yaw_error",
+ "goal_distance",
+ )
+ assert cfg.actions["joint_pos"].clip is None
+ assert rl_cfg.clip_actions is None
+ assert rl_cfg.experiment_name == "t1_target_location_amp_competition_foot"
+
+
+def test_t1_target_location_amp_competition_variants_use_server_physics() -> None:
+ for task_id in (COMPETITION_FOOT_TASK_ID, COMPETITION_COLLISION_TASK_ID):
+ for play in (False, True):
+ cfg = load_env_cfg(task_id, play=play)
+ mujoco_cfg = cfg.sim.mujoco
+
+ assert mujoco_cfg.integrator == "euler"
+ assert mujoco_cfg.cone == "pyramidal"
+ assert mujoco_cfg.impratio == 1.0
+ assert mujoco_cfg.iterations == 100
+ assert mujoco_cfg.ls_iterations == 50
+ assert mujoco_cfg.ccd_iterations == 35
+
+
+def _object_names(
+ model: mujoco.MjModel, objtype: mujoco.mjtObj, count: int
+) -> list[str]:
+ return [mujoco.mj_id2name(model, objtype, idx) or "" for idx in range(count)]
+
+
+def _exclude_body_pairs(model: mujoco.MjModel) -> set[frozenset[str]]:
+ pairs: set[frozenset[str]] = set()
+ for signature in model.exclude_signature:
+ body_1 = int(signature) >> 16
+ body_2 = int(signature) & 0xFFFF
+ pairs.add(
+ frozenset(
+ (
+ mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_BODY, body_1) or "",
+ mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_BODY, body_2) or "",
+ )
+ )
+ )
+ return pairs
+
+
+@pytest.mark.skipif(
+ not COMPETITION_T1_XML.is_file(),
+ reason="set MJLAB_COMPETITION_T1_XML to run simulator parity checks",
+)
+def test_t1_target_location_amp_competition_collision_matches_competition_contacts() -> (
+ None
+):
+ cfg = load_env_cfg(COMPETITION_COLLISION_TASK_ID)
+ play_cfg = load_env_cfg(COMPETITION_COLLISION_TASK_ID, play=True)
+ rl_cfg = load_rl_cfg(COMPETITION_COLLISION_TASK_ID)
+
+ robot_cfg = cfg.scene.entities["robot"]
+ train_model = robot_cfg.spec_fn().compile()
+ competition_model = mujoco.MjModel.from_xml_path(str(COMPETITION_T1_XML))
+
+ train_geoms = _object_names(train_model, mujoco.mjtObj.mjOBJ_GEOM, train_model.ngeom)
+ competition_geoms = _object_names(
+ competition_model, mujoco.mjtObj.mjOBJ_GEOM, competition_model.ngeom
+ )
+ train_collision_geoms = {
+ name
+ for idx, name in enumerate(train_geoms)
+ if train_model.geom_contype[idx] != 0 or train_model.geom_conaffinity[idx] != 0
+ }
+ competition_collision_geoms = {
+ name
+ for idx, name in enumerate(competition_geoms)
+ if (
+ competition_model.geom_contype[idx] != 0
+ or competition_model.geom_conaffinity[idx] != 0
+ )
+ }
+ assert train_collision_geoms == competition_collision_geoms
+ assert not any(name.endswith("_collision") for name in train_collision_geoms)
+ assert _exclude_body_pairs(train_model) == _exclude_body_pairs(competition_model)
+
+ for geom_name in sorted(competition_collision_geoms):
+ train_idx = train_geoms.index(geom_name)
+ competition_idx = competition_geoms.index(geom_name)
+ assert (
+ train_model.geom_type[train_idx] == competition_model.geom_type[competition_idx]
+ )
+ assert (
+ train_model.geom_condim[train_idx]
+ == competition_model.geom_condim[competition_idx]
+ )
+ assert (
+ train_model.geom_contype[train_idx]
+ == competition_model.geom_contype[competition_idx]
+ )
+ assert (
+ train_model.geom_conaffinity[train_idx]
+ == competition_model.geom_conaffinity[competition_idx]
+ )
+ assert tuple(train_model.geom_size[train_idx]) == tuple(
+ competition_model.geom_size[competition_idx]
+ )
+ assert tuple(train_model.geom_pos[train_idx]) == tuple(
+ competition_model.geom_pos[competition_idx]
+ )
+ assert tuple(train_model.geom_friction[train_idx]) == tuple(
+ competition_model.geom_friction[competition_idx]
+ )
+ assert tuple(train_model.geom_solref[train_idx]) == tuple(
+ competition_model.geom_solref[competition_idx]
+ )
+ assert tuple(train_model.geom_solimp[train_idx]) == tuple(
+ competition_model.geom_solimp[competition_idx]
+ )
+
+ train_bodies = _object_names(train_model, mujoco.mjtObj.mjOBJ_BODY, train_model.nbody)
+ competition_bodies = _object_names(
+ competition_model, mujoco.mjtObj.mjOBJ_BODY, competition_model.nbody
+ )
+ for body_name in ("left_foot_link", "right_foot_link"):
+ train_idx = train_bodies.index(body_name)
+ competition_idx = competition_bodies.index(body_name)
+ assert tuple(train_model.body_inertia[train_idx]) == tuple(
+ competition_model.body_inertia[competition_idx]
+ )
+ assert tuple(train_model.body_iquat[train_idx]) == tuple(
+ competition_model.body_iquat[competition_idx]
+ )
+
+ assert all(
+ actuator.viscous_damping == 0.01 for actuator in robot_cfg.articulation.actuators
+ )
+
+ foot_friction_cfg = cfg.events["foot_friction"].params["asset_cfg"]
+ assert foot_friction_cfg.geom_names == r"^(left|right)_foot$"
+ assert "foot_friction" not in play_cfg.events
+ assert tuple(cfg.observations["actor"].terms) == (
+ "base_ang_vel",
+ "projected_gravity",
+ "joint_pos",
+ "joint_vel",
+ "actions",
+ "goal_local_pos",
+ "heading_to_target",
+ "final_yaw_error",
+ "goal_distance",
+ )
+ assert cfg.actions["joint_pos"].clip is None
+ assert rl_cfg.clip_actions is None
+ assert rl_cfg.experiment_name == "t1_target_location_amp_competition_collision"
+
+
+def test_t1_target_location_amp_competition_collision_play_uses_nearfield_stage() -> (
+ None
+):
+ play_cfg = load_env_cfg(COMPETITION_COLLISION_TASK_ID, play=True)
+ goal_pose_cmd = play_cfg.commands["goal_pose"]
+
+ assert goal_pose_cmd.ranges.target_radius == (0.0, 6.0)
+ assert goal_pose_cmd.ranges.target_yaw == (-math.pi, math.pi)
+ assert goal_pose_cmd.ranges.target_angle == (-math.pi, math.pi)
+ assert goal_pose_cmd.ranges.target_radius_buckets is None
+ assert goal_pose_cmd.ranges.target_command_buckets is not None
+ assert tuple(
+ (bucket.radius_range, bucket.yaw_range, bucket.angle_range, bucket.probability)
+ for bucket in goal_pose_cmd.ranges.target_command_buckets
+ ) == (
+ ((0.0, 0.0), (0.0, 0.0), (0.0, 0.0), 0.1),
+ ((0.0, 0.0), (-math.pi, math.pi), (0.0, 0.0), 0.1),
+ ((0.0, 2.0), None, None, 0.3),
+ ((5.0, 6.0), None, None, 0.4),
+ ((2.0, 4.0), None, None, 0.1),
+ )
+ assert goal_pose_cmd.resampling_time_range == (1.0, 4.0)
+ assert goal_pose_cmd.resample_on_near_goal is False
+ assert goal_pose_cmd.resample_on_goal_success is True
+ assert math.isclose(goal_pose_cmd.resample_position_tolerance, 0.08)
+ assert math.isclose(goal_pose_cmd.resample_yaw_tolerance, math.radians(6.0))
+
+ assert play_cfg.rewards["joint_target_limits"].weight == -0.03
+ assert play_cfg.rewards["near_goal_stability"].weight == 0.5
+ assert math.isclose(
+ play_cfg.rewards["near_goal_stability"].params["position_tolerance"],
+ 0.08,
+ )
+ assert math.isclose(
+ play_cfg.rewards["near_goal_stability"].params["yaw_tolerance"],
+ math.radians(6.0),
+ )
+ assert play_cfg.rewards["goal_pose_bonus"].weight == 1.0
+ assert play_cfg.rewards["goal_position_bonus"].weight == 0.5
+ assert math.isclose(
+ play_cfg.rewards["goal_pose_bonus"].params["position_tolerance"], 0.08
+ )
+ assert math.isclose(
+ play_cfg.rewards["goal_pose_bonus"].params["yaw_tolerance"],
+ math.radians(6.0),
+ )
+ assert play_cfg.rewards["goal_pose_bonus"].params["hold_steps"] == 25
+
+
+def test_goal_pose_command_can_resample_early_on_success() -> None:
+ reward_manager = SimpleNamespace(
+ get_term_cfg=lambda name: SimpleNamespace(
+ params={
+ "position_tolerance": 0.08,
+ "yaw_tolerance": math.radians(6.0),
+ "hold_steps": 25,
+ "entity_name": "robot",
+ "max_lin_speed": 0.25,
+ "max_yaw_rate": 0.35,
+ "min_up_z": 0.80,
+ }
+ if name == "goal_pose_bonus"
+ else None
+ )
+ )
+ robot = SimpleNamespace(
+ data=SimpleNamespace(
+ root_link_pos_w=torch.tensor([[0.0, 0.0, 0.7]], dtype=torch.float32),
+ root_link_lin_vel_b=torch.zeros(1, 3, dtype=torch.float32),
+ root_link_ang_vel_b=torch.zeros(1, 3, dtype=torch.float32),
+ projected_gravity_b=torch.tensor([[0.0, 0.0, -1.0]], dtype=torch.float32),
+ )
+ )
+ env = SimpleNamespace(
+ num_envs=1,
+ device="cpu",
+ reward_manager=reward_manager,
+ scene={"robot": robot},
+ episode_length_buf=torch.tensor([1], dtype=torch.long),
+ )
+ command = GoalPoseCommand.__new__(GoalPoseCommand)
+ command._env = env
+ command.cfg = SimpleNamespace(
+ entity_name="robot",
+ resample_on_goal_success=True,
+ resample_on_near_goal=False,
+ resample_position_tolerance=0.08,
+ resample_yaw_tolerance=math.radians(6.0),
+ )
+ command.distance_to_goal = torch.tensor([0.02], dtype=torch.float32)
+ command.final_heading_error = torch.tensor([math.radians(2.0)], dtype=torch.float32)
+ command._resample_on_next_step = torch.zeros(1, dtype=torch.bool)
+
+ command._queue_goal_success_resample()
+ assert command._resample_on_next_step.tolist() == [False]
+
+ env._goal_pose_prev_distance = torch.tensor([0.02], dtype=torch.float32)
+ env._goal_pose_prev_yaw_abs = torch.tensor([math.radians(2.0)], dtype=torch.float32)
+ env._goal_pose_distance_progress = torch.zeros(1, dtype=torch.float32)
+ env._goal_pose_yaw_progress = torch.zeros(1, dtype=torch.float32)
+ env._goal_pose_hold_steps = torch.tensor([24], dtype=torch.long)
+ env._goal_pose_success_hold_steps = torch.tensor([24], dtype=torch.long)
+ env._goal_pose_success_hold_last_step = torch.tensor([24], dtype=torch.long)
+ env._goal_pose_state_last_step = torch.tensor([24], dtype=torch.long)
+ env._goal_pose_just_resampled = torch.tensor([False], dtype=torch.bool)
+ env.episode_length_buf = torch.tensor([25], dtype=torch.long)
+ command._queue_goal_success_resample()
+ assert command._resample_on_next_step.tolist() == [True]
+
+
+def test_goal_success_hold_requires_consecutive_full_success_steps() -> None:
+ env = SimpleNamespace(
+ num_envs=1,
+ device="cpu",
+ episode_length_buf=torch.tensor([1], dtype=torch.long),
+ )
+
+ hold = update_goal_pose_success_hold_state(
+ env,
+ success_now=torch.tensor([True]),
+ )
+ assert hold.tolist() == [1]
+
+ env.episode_length_buf[:] = 2
+ hold = update_goal_pose_success_hold_state(
+ env,
+ success_now=torch.tensor([False]),
+ )
+ assert hold.tolist() == [0]
+
+ env.episode_length_buf[:] = 3
+ hold = update_goal_pose_success_hold_state(
+ env,
+ success_now=torch.tensor([True]),
+ )
+ assert hold.tolist() == [1]
+
+ # Multiple reward/command consumers in one controller step must not double count.
+ hold = update_goal_pose_success_hold_state(
+ env,
+ success_now=torch.tensor([True]),
+ )
+ assert hold.tolist() == [1]
diff --git a/tests/test_tracking.py b/tests/test_tracking.py
new file mode 100644
index 0000000..1c7ca15
--- /dev/null
+++ b/tests/test_tracking.py
@@ -0,0 +1,42 @@
+"""Tests for Booster T1 tracking task registration/configuration."""
+
+from __future__ import annotations
+
+import mjlab_playground # noqa: F401
+from mjlab.tasks.registry import list_tasks, load_env_cfg, load_rl_cfg
+from mjlab_playground.tasks.tracking.config.t1.env_cfgs import (
+ booster_t1_flat_tracking_env_cfg,
+)
+
+
+def test_tracking_task_registered() -> None:
+ assert "Mjlab-Tracking-Flat-Booster-T1" in list_tasks()
+
+
+def test_tracking_task_configs_load() -> None:
+ env_cfg = load_env_cfg("Mjlab-Tracking-Flat-Booster-T1")
+ rl_cfg = load_rl_cfg("Mjlab-Tracking-Flat-Booster-T1")
+ assert "motion" in env_cfg.commands
+ assert env_cfg.commands["motion"].anchor_body_name == "Trunk"
+ assert env_cfg.commands["motion"].body_names[0] == "Trunk"
+ assert "base_ang_vel_exceed" in env_cfg.terminations
+ assert rl_cfg.experiment_name == "t1_tracking"
+
+
+def test_tracking_play_cfg_overrides() -> None:
+ cfg = booster_t1_flat_tracking_env_cfg(play=True)
+ motion = cfg.commands["motion"]
+ assert cfg.episode_length_s == int(1e9)
+ assert "push_robot" not in cfg.events
+ assert motion.pose_range == {}
+ assert motion.velocity_range == {}
+ assert motion.sampling_mode == "start"
+
+
+def test_tracking_actor_terms_without_state_estimation() -> None:
+ cfg = booster_t1_flat_tracking_env_cfg(has_state_estimation=False)
+ actor_terms = cfg.observations["actor"].terms
+ assert "motion_anchor_pos_b" not in actor_terms
+ assert "base_lin_vel" not in actor_terms
+ assert "motion_anchor_ori_b" in actor_terms
+ assert "base_ang_vel" in actor_terms
diff --git a/tests/test_velocity.py b/tests/test_velocity.py
new file mode 100644
index 0000000..27983d6
--- /dev/null
+++ b/tests/test_velocity.py
@@ -0,0 +1,160 @@
+"""Tests for the velocity task configurations."""
+
+from __future__ import annotations
+
+import torch
+from mjlab.tasks.registry import list_tasks
+from mjlab_playground.tasks.velocity import mdp as velocity_local_mdp
+from mjlab_playground.tasks.velocity.config.t1.env_cfgs import booster_t1_flat_env_cfg
+from mjlab_playground.tasks.velocity.mdp.curriculums import commands_vel
+from mjlab_playground.tasks.velocity.mdp.observations import generated_commands
+from mjlab_playground.tasks.velocity.mdp.rewards import _is_forward_command_active
+
+
+def test_velocity_task_registration() -> None:
+ import mjlab_playground # noqa: F401
+
+ tasks = list_tasks()
+ assert "Mjlab-Velocity-Flat-Booster-T1" in tasks
+ assert "Mjlab-Velocity-Rough-Booster-T1" not in tasks
+ assert "Mjlab-VelocityYaw-Flat-Booster-T1" not in tasks
+ assert "Mjlab-VelocityRaw-Flat-Booster-T1" not in tasks
+
+
+def test_t1_velocity_play_cfg_resamples_visible_commands() -> None:
+ cfg = booster_t1_flat_env_cfg(play=True)
+ twist = cfg.commands["twist"]
+ assert twist.resampling_time_range == (1.0, 1.5)
+ assert twist.rel_standing_envs == 0.0
+ assert twist.rel_lateral_envs == 0.0
+ assert twist.ranges.lin_vel_x == (2.0, 3.0)
+ assert twist.ranges.lin_vel_y == (0.0, 0.0)
+ assert twist.ranges.heading == (-0.5, 0.5)
+
+
+def test_t1_velocity_uses_mixed_stand_x_heading_and_lateral_commands() -> None:
+ cfg = booster_t1_flat_env_cfg()
+ twist = cfg.commands["twist"]
+ assert twist.heading_command is True
+ assert twist.rel_lateral_envs == 0.2
+ assert twist.rel_heading_envs == 1.0
+ assert twist.rel_forward_envs == 0.0
+ assert twist.ranges.lin_vel_y == (-0.3, 0.3)
+ assert twist.ranges.ang_vel_z == (-0.5, 0.5)
+ assert twist.ranges.heading == (-torch.pi, torch.pi)
+
+ assert (
+ cfg.observations["actor"].terms["command"].func
+ is velocity_local_mdp.generated_commands
+ )
+ assert cfg.rewards["track_heading"].func is velocity_local_mdp.track_heading_command
+
+ assert set(cfg.curriculum.keys()) == {"command_vel"}
+ stages = cfg.curriculum["command_vel"].params["velocity_stages"]
+ assert stages[0]["lin_vel_x"] == (-0.5, 1.5)
+ assert stages[0]["lin_vel_y"] == (-0.3, 0.3)
+ assert stages[0]["heading"] == (-0.5, 0.5)
+ assert "ang_vel_z" not in stages[0]
+ assert stages[1]["lin_vel_x"] == (-1.0, 2.5)
+ assert stages[1]["lin_vel_y"] == (-0.6, 0.6)
+ assert stages[1]["heading"] == (-0.7, 0.7)
+ assert "ang_vel_z" not in stages[1]
+ assert stages[2]["lin_vel_x"] == (0.0, 3.0)
+ assert stages[2]["lin_vel_y"] == (-1.0, 1.0)
+ assert stages[2]["heading"] == (-torch.pi * 0.5, torch.pi * 0.5)
+ assert "ang_vel_z" not in stages[2]
+
+
+def test_generated_commands_returns_x_y_plus_heading_encoding() -> None:
+ class FakeCommandTerm:
+ def __init__(self) -> None:
+ self.heading_target = torch.tensor(
+ [0.0, torch.pi / 2, torch.pi], dtype=torch.float32
+ )
+ self.is_heading_env = torch.tensor([False, True, False])
+ self.is_standing_env = torch.tensor([True, False, False])
+ self.robot = type(
+ "Robot",
+ (),
+ {
+ "data": type(
+ "Data",
+ (),
+ {"heading_w": torch.tensor([1.2, 0.0, 0.4], dtype=torch.float32)},
+ )()
+ },
+ )()
+
+ class FakeCommandManager:
+ def get_command(self, name: str) -> torch.Tensor:
+ assert name == "twist"
+ return torch.tensor(
+ [
+ [0.0, 0.0, 0.0],
+ [1.1, 0.0, -0.1],
+ [0.0, -0.6, 0.0],
+ ],
+ dtype=torch.float32,
+ )
+
+ def get_term(self, name: str) -> FakeCommandTerm:
+ assert name == "twist"
+ return FakeCommandTerm()
+
+ env = type("Env", (), {"command_manager": FakeCommandManager(), "scene": {}})()
+ obs = generated_commands(env, command_name="twist")
+ expected = torch.tensor(
+ [
+ [0.0, 0.0, 0.0, 1.0],
+ [1.1, 0.0, 1.0, 0.0],
+ [0.0, -0.6, 0.0, 1.0],
+ ],
+ dtype=torch.float32,
+ )
+ assert torch.allclose(obs, expected, atol=1e-5)
+
+
+def test_commands_vel_logs_heading_ranges_only() -> None:
+ cfg = booster_t1_flat_env_cfg()
+ command_term = type("CommandTerm", (), {"cfg": cfg.commands["twist"]})()
+ env = type(
+ "Env",
+ (),
+ {
+ "common_step_counter": 120000,
+ "command_manager": type(
+ "CommandManager", (), {"get_term": lambda self, name: command_term}
+ )(),
+ },
+ )()
+ metrics = commands_vel(
+ env,
+ env_ids=torch.tensor([], dtype=torch.long),
+ command_name="twist",
+ velocity_stages=cfg.curriculum["command_vel"].params["velocity_stages"],
+ )
+ assert set(metrics.keys()) == {
+ "lin_vel_x_min",
+ "lin_vel_x_max",
+ "lin_vel_y_min",
+ "lin_vel_y_max",
+ "heading_min",
+ "heading_max",
+ }
+
+
+def test_forward_command_gate_ignores_y_and_heading() -> None:
+ command = torch.tensor(
+ [
+ [0.0, 0.8, 0.0],
+ [0.0, 0.0, 0.7],
+ [0.2, 0.8, 0.7],
+ [-0.2, 0.0, 0.0],
+ ],
+ dtype=torch.float32,
+ )
+
+ active = _is_forward_command_active(command, command_threshold=0.1)
+
+ expected = torch.tensor([0.0, 0.0, 1.0, 1.0], dtype=torch.float32)
+ assert torch.equal(active, expected)
diff --git a/third_party_licenses/RCSSServerMJ-MIT.txt b/third_party_licenses/RCSSServerMJ-MIT.txt
new file mode 100644
index 0000000..d91f400
--- /dev/null
+++ b/third_party_licenses/RCSSServerMJ-MIT.txt
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 The RCSSServerMJ authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/uv.lock b/uv.lock
index c807db1..89c2558 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,62 +1,36 @@
version = 1
revision = 3
-requires-python = ">=3.13"
+requires-python = "==3.13.*"
resolution-markers = [
- "platform_machine != 's390x' and sys_platform != 'darwin'",
- "platform_machine == 's390x' and sys_platform != 'darwin'",
- "platform_machine != 's390x' and sys_platform == 'darwin'",
- "platform_machine == 's390x' and sys_platform == 'darwin'",
+ "sys_platform != 'darwin'",
+ "sys_platform == 'darwin'",
]
-[manifest]
-overrides = [{ name = "mujoco", specifier = ">=3.6.0.dev0", index = "https://py.mujoco.org/" }]
-
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-
|
-| `Mjlab-Getup-Flat-Booster-T1` | Booster T1 | Fall recovery on flat terrain | 
|
-
-## Getting Started
+| Task ID | Robot | Description |
+|---|---|---|
+| `Mjlab-Getup-Flat-Unitree-Go1` | Unitree Go1 | Upstream flat-ground get-up |
+| `Mjlab-Getup-Flat-Booster-T1` | Booster T1 | Upstream flat-ground get-up |
+| `Mjlab-Getup-Flat-Booster-K1` | Booster K1 | Flat-ground get-up |
+| `Mjlab-Velocity-Flat-Booster-T1` | Booster T1 | Flat-ground velocity tracking |
+| `Mjlab-Tracking-Flat-Booster-T1` | Booster T1 | Reference-motion tracking |
+| `Mjlab-TargetLocationAmp-Flat-Booster-K1` | Booster K1 | Target-location locomotion with AMP |
+| `Mjlab-TargetLocationAmp-Flat-Booster-T1` | Booster T1 | Target-location locomotion with AMP |
+
+There is one public Booster velocity task: `Mjlab-Velocity-Flat-Booster-T1`.
+Rough-terrain, raw-observation, and yaw-only builders remain implementation
+details rather than separately advertised environments. There is currently no
+K1 tracking task in this fork. The two additional T1 target-location task IDs
+ending in `CompetitionFoot` and `CompetitionCollision` are deployment-oriented
+contact/collision variants.
+
+## Getting started
```bash
-git clone https://github.com/mujocolab/mjlab_playground.git && cd mjlab_playground
+git clone https://github.com/66Leslie/mjlab_playground.git
+cd mjlab_playground
uv sync
```
-Train a task:
+List registered tasks:
+
+```bash
+uv run list-envs
+```
+
+Train and play a task:
```bash
-uv run train