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@A2R-Lab

The Accessible and Accelerated Robotics Lab (A²R Lab)

The Accessible and Accelerated Robotics Lab (A²R Lab)

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The A²R Lab focusses on focuses on optimizing robotic systems at all scales by developing, optimizing, implementing, and evaluating next-generation algorithms and edge computational systems, through algorithm-hardware-software co-design (e.g., MPCGPU, GRiD, TinyMPC). As such, our research is at the intersection of Robotics and Computer Architecture, Embedded Systems, Numerical Optimization, and Machine Learning.

We also want to promote a responsible, sustainable, and accessible future for robotics and edge computing, including the development of new interdisciplinary, project-based, open-access courses that lower the barriers to entry for cutting-edge topics like robotics, parallel programming, and embedded machine learning (e.g., Global TinyML Education, Parallel Optimization for Robotics).

To learn more about our lab please visit our website: https://a2r-lab.org/.

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  1. GRiD GRiD Public

    Forked from robot-acceleration/GRiD

    A GPU accelerated library for computing rigid body dynamics with analytical gradients

    Python 6 6

  2. MPCGPU MPCGPU Public

    Numerical experiments for the paper: "MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPU"

    Cuda 53 4

  3. GBD-PCG GBD-PCG Public

    A GPU-Accelerated Block-Tridiagonal Matrix Preconditioned Conjugate Gradient Solver

    Cuda 4 2

  4. Just-Round Just-Round Public

    Supplementary code for "Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic Locomotion"

    Python 2

  5. DiffCompressDRL DiffCompressDRL Public

    Source code and numerical experiments for the paper: "Differentially Encoded Observation Spaces for Perceptive Reinforcement Learning"

    Python

  6. TrajoptMPCReference TrajoptMPCReference Public

    A Python reference implementation of trajectory optimization (trajopt) algorithms and model predictive control (MPC).

    Python 5 5

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