複雑地形における四足歩行ロボットの適応的転倒復帰コントローラの学習
Learning an Adaptive Fall Recovery Controller for Quadrupeds on Complex Terrains
深層強化学習を用いて、岩場や急斜面などの複雑地形で四足歩行ロボットが転倒から復帰できる適応的コントローラを訓練し、実機への転移とシミュレーションで有効性を検証した。
著者: Yidan Lu, Yinzhao Dong, Ji Ma, Jiahui Zhang, Peng Lu
分類: cs.RO
原文アブストラクト
Legged robots have shown promise in locomotion complex environments, but recovery from falls on challenging terrains remains a significant hurdle. This paper presents an Adaptive Fall Recovery (AFR) controller for quadrupedal robots on challenging terrains such as rocky, breams, steep slopes, and irregular stones. We leverage deep reinforcement learning to train the AFR, which can adapt to a wide range of terrain geometries and physical properties. Our method demonstrates improvements over existing approaches, showing promising results in recovery scenarios on challenging terrains. We trained our method in Isaac Gym using the Go1 and directly transferred it to several mainstream quadrupedal platforms, such as Spot and ANYmal. Additionally, we validated the controller's effectiveness in Gazebo. Our results indicate that the AFR controller generalizes well to complex terrains and outperforms baseline methods in terms of success rate and recovery speed.
