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arXiv · 2310.15583

Learning Agility and Adaptive Legged Locomotion via Curricular Hindsight Reinforcement Learning

Sicen Li·Yiming Pang·Panju Bai·Zhaojin Liu·Jiawei Li·Shihao Hu·Liquan Wang·Gang Wang
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Abstract

Agile and adaptive maneuvers such as fall recovery, high-speed turning, and sprinting in the wild are challenging for legged systems. We propose a Curricular Hindsight Reinforcement Learning (CHRL) that learns an end-to-end tracking controller that achieves powerful agility and adaptation for the legged robot. The two key components are (I) a novel automatic curriculum strategy on task difficulty and (ii) a Hindsight Experience Replay strategy adapted to legged locomotion tasks. We demonstrated successful agile and adaptive locomotion on a real quadruped robot that performed fall recovery autonomously, coherent trotting, sustained outdoor speeds up to 3.45 m/s, and tuning speeds up to 3.2 rad/s. This system produces adaptive behaviours responding to changing situations and unexpected disturbances on natural terrains like grass and dirt.

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Sicen Li, Yiming Pang, Panju Bai, Zhaojin Liu, Jiawei Li, Shihao Hu, Liquan Wang, Gang Wang. 2023-10-24. Learning Agility and Adaptive Legged Locomotion via Curricular Hindsight Reinforcement Learning. https://arxiv.org/abs/2310.15583

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