選択的敵対的動作事前分布を用いたヒューマノイドロボットのマルチ歩容学習
Multi-Gait Learning for Humanoid Robots Using Reinforcement Learning with Selective Adversarial Motion Prior
ヒューマノイドロボットが歩行、行進、走行、階段昇降、跳躍の5つの歩容を統一的な強化学習フレームワークで習得する手法を提案。安定性重視の歩容にはAMPを適用し、動的歩容には適用しない選択的AMP戦略により、全歩容で性能が向上することを示した。
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著者: Yuanye Wu, Keyi Wang, Linqi Ye, Boyang Xing
分類: cs.RO, cs.AI
原文アブストラクト
Learning diverse locomotion skills for humanoid robots in a unified reinforcement learning framework remains challenging due to the conflicting requirements of stability and dynamic expressiveness across different gaits. We present a multi-gait learning approach that enables a humanoid robot to master five distinct gaits -- walking, goose-stepping, running, stair climbing, and jumping -- using a consistent policy structure, action space, and reward formulation. The key contribution is a selective Adversarial Motion Prior (AMP) strategy: AMP is applied to periodic, stability-critical gaits (walking, goose-stepping, stair climbing) where it accelerates convergence and suppresses erratic behavior, while being deliberately omitted for highly dynamic gaits (running, jumping) where its regularization would over-constrain the motion. Policies are trained via PPO with domain randomization in simulation and deployed on a physical 12-DOF humanoid robot through zero-shot sim-to-real transfer. Quantitative comparisons demonstrate that selective AMP outperforms a uniform AMP policy across all five gaits, achieving faster convergence, lower tracking error, and higher success rates on stability-focused gaits without sacrificing the agility required for dynamic ones.
