強化学習による高速移動の実現
Rapid Locomotion via Reinforcement Learning
強化学習で訓練したニューラルネットワーク制御器をシミュレーションから実機に転移し、MIT Mini Cheetahで最大3.9m/sの高速走行と旋回を実現した。
著者: Gabriel B Margolis, Ge Yang, Kartik Paigwar, Tao Chen, Pulkit Agrawal
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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/
