メタ戦略最適化による脚式ロボットの高速適応学習
Learning Fast Adaptation with Meta Strategy Optimization
訓練とテストで同じ適応プロセスを共有するメタ学習手法MSOを提案し、四足歩行ロボットがsim2realやモータ故障、坂道など新環境に少数試行で適応できることを実機で示した。
著者: Wenhao Yu, Jie Tan, Yunfei Bai, Erwin Coumans, Sehoon Ha
分類: cs.RO, cs.LG
原文アブストラクト
The ability to walk in new scenarios is a key milestone on the path toward real-world applications of legged robots. In this work, we introduce Meta Strategy Optimization, a meta-learning algorithm for training policies with latent variable inputs that can quickly adapt to new scenarios with a handful of trials in the target environment. The key idea behind MSO is to expose the same adaptation process, Strategy Optimization (SO), to both the training and testing phases. This allows MSO to effectively learn locomotion skills as well as a latent space that is suitable for fast adaptation. We evaluate our method on a real quadruped robot and demonstrate successful adaptation in various scenarios, including sim-to-real transfer, walking with a weakened motor, or climbing up a slope. Furthermore, we quantitatively analyze the generalization capability of the trained policy in simulated environments. Both real and simulated experiments show that our method outperforms previous methods in adaptation to novel tasks.
