Web: http://arxiv.org/abs/2205.02824

May 6, 2022, 1:11 a.m. | Gabriel B Margolis, Ge Yang, Kartik Paigwar, Tao Chen, Pulkit Agrawal

cs.LG updates on arXiv.org arxiv.org

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) …

arxiv learning reinforcement reinforcement learning

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