進化的メタ学習による迅速に適応可能な脚式ロボット
Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning
進化的戦略に基づくメタ学習とノイズに強いバッチ山登り適応を組み合わせ、四足歩行ロボットが3分未満の実データで動力学変化に素早く適応できる手法を提案した。
著者: Xingyou Song, Yuxiang Yang, Krzysztof Choromanski, Ken Caluwaerts, Wenbo Gao, Chelsea Finn, Jie Tan
分類: cs.RO, cs.LG, cs.NE
原文アブストラクト
Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world. In this work, we present a new meta-learning method that allows robots to quickly adapt to changes in dynamics. In contrast to gradient-based meta-learning algorithms that rely on second-order gradient estimation, we introduce a more noise-tolerant Batch Hill-Climbing adaptation operator and combine it with meta-learning based on evolutionary strategies. Our method significantly improves adaptation to changes in dynamics in high noise settings, which are common in robotics applications. We validate our approach on a quadruped robot that learns to walk while subject to changes in dynamics. We observe that our method significantly outperforms prior gradient-based approaches, enabling the robot to adapt its policy to changes based on less than 3 minutes of real data.
