Adaptive-MHE：基于移动时域估计的采样型自适应腿式移动操作模型预测控制

中文摘要 AI 辅助

腿式机器人已展现出穿越各种地形的卓越能力，然而生成有效的移动操作行为仍然具有挑战性。一个关键难点在于，物体和地形参数通常对机器人而言是未知的，这些参数与其仿真对应物之间的不匹配会引入仿真到现实的差距，从而降低控制性能。经典的系统辨识（Sys-ID）方法通常假设动力学是可微的，而这一假设对于接触丰富的腿式系统并不成立。基于采样的系统辨识通过大规模并行回放直接匹配仿真与记录的状态轨迹，从而避免了这一限制，但现有方法通常离线应用，且无法随环境条件变化而自适应。我们提出了Adaptive-MHE，一种基于移动时域估计（MHE）的在线采样型系统辨识框架，该框架估计环境中物体和地形的物理参数（如质量、摩擦力），并将该估计与基于采样的模型预测控制器相结合，从而在变化和不确定的环境中实现自适应的移动操作。在仿真和硬件实验中，我们的框架始终优于基线方法，并达到了与能够获取真实参数的控制器的性能相当的水平。

英文摘要

Legged robots have demonstrated a remarkable ability to traverse various terrains, yet generating effective loco-manipulation behaviors remains challenging. A key difficulty is that object and terrain parameters are typically unknown to the robot, and mismatches between these parameters and their simulated counterparts introduce a sim-to-real gap that degrades control performance. Classical system identification (Sys-ID) methods often assume differentiable dynamics, an assumption that does not hold for contact-rich legged systems. Sampling-based Sys-ID avoids this restriction by directly matching simulated and recorded state trajectories through massively parallel rollouts, but existing approaches are typically applied offline and do not adapt as environmental conditions change. We present Adaptive-MHE an online sampling-based Sys-ID framework, based on moving horizon estimation (MHE), that estimates the physical parameters of objects and terrain in the environment (e.g., mass, friction) and couples this estimate with a sampling-based model predictive controller, enabling adaptive loco-manipulation in changing and uncertain environments. In simulation and hardware experiments, our framework consistently outperforms baselines and matches the performance of a controller with access to ground-truth parameters.

发表机构

• University of Calgary（卡尔加里大学）
• Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM)（慕尼黑工业大学机器人与机器智能研究所（MIRMI））

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