CITATION.cff

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cff-version: 1.2.0
title: 'DIAL-MPC: Diffusion-Inspired Annealing For Legged MPC'
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Haoru
    family-names: Xue
    email: haoru-xue@berkeley.edu
    affiliation: University of California Berkeley
    orcid: 'https://orcid.org/0009-0009-1195-2254'
  - given-names: Chaoyi
    family-names: Pan
    email: chaoyip@andrew.cmu.edu
    affiliation: Carnegie Mellon University
  - given-names: Zeji
    family-names: Yi
    email: zejiy@andrew.cmu.edu
    affiliation: Carnegie Mellon University
  - given-names: Guannan
    family-names: Qu
    email: gqu@andrew.cmu.edu
    affiliation: Carnegie Mellon University
    orcid: 'https://orcid.org/0000-0002-5466-3550'
  - given-names: Guanya
    family-names: Shi
    email: guanyas@andrew.cmu.edu
    affiliation: Carnegie Mellon University
    orcid: 'https://orcid.org/0000-0002-9075-3705'
identifiers:
  - type: doi
    value: 10.48550/arXiv.2409.15610
repository-code: 'https://github.com/LeCAR-Lab/dial-mpc'
url: 'https://lecar-lab.github.io/dial-mpc/'
abstract: >-
  Due to high dimensionality and non-convexity, real-time
  optimal control using full-order dynamics models for
  legged robots is challenging. Therefore, Nonlinear Model
  Predictive Control (NMPC) approaches are often limited to
  reduced-order models. Sampling-based MPC has shown
  potential in nonconvex even discontinuous problems, but
  often yields suboptimal solutions with high variance,
  which limits its applications in high-dimensional
  locomotion. This work introduces DIAL-MPC
  (Diffusion-Inspired Annealing for Legged MPC), a
  sampling-based MPC framework with a novel diffusion-style
  annealing process. Such an annealing process is supported
  by the theoretical landscape analysis of Model Predictive
  Path Integral Control (MPPI) and the connection between
  MPPI and single-step diffusion. Algorithmically, DIAL-MPC
  iteratively refines solutions online and achieves both
  global coverage and local convergence. In quadrupedal
  torque-level control tasks, DIAL-MPC reduces the tracking
  error of standard MPPI by 13.4 times and outperforms
  reinforcement learning (RL) policies by 50% in challenging
  climbing tasks without any training. In particular,
  DIAL-MPC enables precise real-world quadrupedal jumping
  with payload. To the best of our knowledge, DIAL-MPC is
  the first training-free method that optimizes over
  full-order quadruped dynamics in real-time.
keywords:
  - mpc
  - optimal control
  - diffusion
  - humanoid
  - legged robot
  - quadruped
license: Apache-2.0
preferred-citation:
  type: generic
  authors:
    - given-names: Haoru
      family-names: Xue
      email: haoru-xue@berkeley.edu
      affiliation: University of California Berkeley
      orcid: 'https://orcid.org/0009-0009-1195-2254'
    - given-names: Chaoyi
      family-names: Pan
      email: chaoyip@andrew.cmu.edu
      affiliation: Carnegie Mellon University
    - given-names: Zeji
      family-names: Yi
      email: zejiy@andrew.cmu.edu
      affiliation: Carnegie Mellon University
    - given-names: Guannan
      family-names: Qu
      email: gqu@andrew.cmu.edu
      affiliation: Carnegie Mellon University
      orcid: 'https://orcid.org/0000-0002-5466-3550'
    - given-names: Guanya
      family-names: Shi
      email: guanyas@andrew.cmu.edu
      affiliation: Carnegie Mellon University
      orcid: 'https://orcid.org/0000-0002-9075-3705'
  url: https://arxiv.org/abs/2409.15610
  month: 9
  year: 2024
  title: Full-Order Sampling-Based MPC for Torque-Level Locomotion Control via Diffusion-Style Annealing