LocoWM: 世界モデル誘導型残差適応による高精度歩行制御
LocoWM: High-Precision Locomotion through World-Model-Guided Residual Adaptation
行動条件付き世界モデルで将来の身体状態を予測し、その予測に基づく残差アダプタで先回りして行動を補正することで、高精度な歩行制御を実現するフレームワークを提案。
著者: Zijie Zhao, Shengqian Chen, Xiaoxu Wang, Han Jiang, Yuanheng Zhu, Dongbin Zhao
分類: cs.RO
原文アブストラクト
High-precision locomotion combines motion-command tracking with precise regulation of task-relevant physical states, enabling robots to interact reliably with their surroundings during motion. Joint end-to-end optimization can leave precision objectives insufficiently optimized, while reactive residual control adjusts actions only after deviations become observable. We present \textbf{LocoWM}, a world-model-guided preactive residual adaptation framework for high-precision locomotion. A base policy provides command-following locomotion, while an action-conditioned world model predicts a sequence of future physical states from proprioceptive history and the proposed base action. A residual adapter conditions on this predicted sequence to generate additive action corrections that compensate for anticipated deviations. Two-stage training first learns locomotion and action-conditioned dynamics, then freezes both modules while training the adapter, separating locomotion acquisition from precision adaptation. Experiments spanning terrain leveling, acceleration compensation, and push recovery demonstrate improved control precision and disturbance robustness over end-to-end and reactive residual baselines. Demos and code are available at: https://zhaozijie2022.github.io/LocoWM