LocoWM：基于世界模型引导的残差自适应实现高精度运动
LocoWM: High-Precision Locomotion through World-Model-Guided Residual Adaptation
- University of Chinese Academy of Sciences（中国科学院大学）
- Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
- Beijing University of Posts and Telecommunications（北京邮电大学）
- Beijing Jiaotong University（北京交通大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
LocoWM通过世界模型预测未来状态，引导残差适配器预主动修正动作，实现高精度运动，并在实验中提升控制精度与抗扰性。
AI中文摘要：
高精度运动结合了运动指令跟踪与任务相关物理状态的精确调节，使机器人在运动过程中能够可靠地与其周围环境进行交互。端到端的联合优化可能使精度目标得不到充分优化，而反应式残差控制仅在偏差变得可观测后才调整动作。我们提出了LocoWM，一种用于高精度运动的世界模型引导的预主动残差自适应框架。基础策略提供指令跟随运动，而动作条件世界模型根据本体感觉历史和所提出的基础动作预测一系列未来物理状态。残差适配器以该预测序列为条件，生成附加的动作修正，以补偿预期偏差。两阶段训练首先学习运动和动作条件动力学，然后冻结这两个模块同时训练适配器，将运动获取与精度自适应分离。涵盖地形平整、加速度补偿和推力恢复的实验表明，与端到端和反应式残差基线相比，控制精度和扰动鲁棒性均有提高。演示和代码可在以下网址获取：此https URL
英文摘要：
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
