8分で歩き出す：四足歩行ロボットの全方位移動を実機で効率的に学習
Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion
オフポリシー強化学習アルゴリズムCrossQを用いて、四足ロボットの全方位歩行を実機上でわずか8分のリアルタイム学習で獲得するフレームワークを提案した。
著者: Nico Bohlinger, Jonathan Kinzel, Daniel Palenicek, Lukasz Antczak, Jan Peters
分類: cs.RO, cs.LG
原文アブストラクト
On-robot Reinforcement Learning is a promising approach to train embodiment-aware policies for legged robots. However, the computational constraints of real-time learning on robots pose a significant challenge. We present a framework for efficiently learning quadruped locomotion in just 8 minutes of raw real-time training utilizing the sample efficiency and minimal computational overhead of the new off-policy algorithm CrossQ. We investigate two control architectures: Predicting joint target positions for agile, high-speed locomotion and Central Pattern Generators for stable, natural gaits. While prior work focused on learning simple forward gaits, our framework extends on-robot learning to omnidirectional locomotion. We demonstrate the robustness of our approach in different indoor and outdoor environments.
