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Rapid Locomotion via Reinforcement Learning

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arxiv 2205.02824 v1 pith:7VHAHFLE submitted 2022-05-05 cs.RO cs.AIcs.LG

Gabriel B Margolis , Ge Yang , Kartik Paigwar , Tao Chen , Pulkit Agrawal This is my paper · ORCID
classification cs.ROcs.AIcs.LG
keywords agilitycontrollerlearningreinforcementsystemachievesadaptiveagile
verification ladder T0 review T1 audit T2 compute T3 formal
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Agile maneuvers such as sprinting and high-speed turning in the wild are challenging for legged robots. We present an end-to-end learned controller that achieves record agility for the MIT Mini Cheetah, sustaining speeds up to 3.9 m/s. This system runs and turns fast on natural terrains like grass, ice, and gravel and responds robustly to disturbances. Our controller is a neural network trained in simulation via reinforcement learning and transferred to the real world. The two key components are (i) an adaptive curriculum on velocity commands and (ii) an online system identification strategy for sim-to-real transfer leveraged from prior work. Videos of the robot's behaviors are available at: https://agility.csail.mit.edu/

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Forward citations

Cited by 6 Pith papers

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  2. GenTrack: Physical Alignment for Robot-Native Motion Generation and Zero-Shot Humanoid Tracking

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  3. Q-learning-based Model-free Safety Filter

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  4. ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills

    cs.RO 2025-02 conditional novelty 5.0 of 10

    ASAP trains a residual action model on real-world rollouts and fine-tunes simulation policies through it, reducing humanoid whole-body motion tracking error in sim-to-real transfer.

  5. Mobile-TeleVision: Predictive Motion Priors for Humanoid Whole-Body Control

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    A decoupled humanoid controller combines IK-based arm control with an RL locomotion policy conditioned on a CVAE motion prior, improving manipulation precision while maintaining walking stability.

  6. GainAdaptor: Learning Quadrupedal Locomotion with Dual Actors for Adaptable and Energy-Efficient Walking on Various Terrains

    cs.RO 2024-12 conditional novelty 4.0 of 10

    GainAdaptor learns to adjust both joint positions and PD gains with two neural network actors, cutting power use on a Unitree Go1 across varied terrains.

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