Problem
Research questions and friction points this paper is trying to address.
legged robots
loco-manipulation
sim-to-real gap
system identification
adaptive control
Adaptive-MHE : A Sampling-Based Adaptive MPC for Legged Loco-Manipulation via Moving Horizon Estimation
🤖 AI Summary
研究提出Adaptive-MHE方法，基于移动视界估计在线估计环境物理参数，并结合采样模型预测控制，以解决腿部机器人在未知环境下的有效运动操作问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adaptive-MHE
Moving Horizon Estimation
Sampling-based Sys-ID
Online Adaptation
Legged Loco-Manipulation
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