外观
论文arXiv 2610.2217613 天前
通过全身模型预测控制与在线参数估计实现惯性显著的人形机器人遥操作
原文链接：Inertially Significant Humanoid Teleoperation via Whole Body Model Predictive Control and Online Parameter Estimation（arXiv 2610.22176 · 发布日期：2026-10-15）
Inertially Significant Humanoid Teleoperation via Whole Body Model Predictive Control and Online Parameter Estimation
针对重工具或大载荷导致的惯性变化，结合WB-MPC与EKF在线估计参数，并在人形系统仿真和硬件实验中验证。
核心要点
- 重工具使用或携带大载荷会改变惯性参数，单靠反馈控制难以忽略其影响。
- 框架将WB-MPC与EKF结合，在线估计物理一致的时变惯性参数。
- 估计结果输入完整刚体动力学模型，为外部负载任务提供更准确前馈力矩。
- VR界面让操作者提供任务参考，并将动态平衡交由WB-MPC处理。
验证范围与局限：验证范围为人形系统上的仿真与硬件实验。
英文摘要 (Abstract)
The effective deployment of humanoid robots in practical tasks of interest will require a capability of sensing and adapting to variations in inertial parameters, as occurs during heavy tool use or when carrying significant payload. Such payloads constitute a significant proportion of the combined payload-robot system, the effects of which cannot be ignored by delegating to feedback control. This paper showcases a general solution to this problem by integrating a whole body model predictive controller (WB-MPC) with an extended Kalman filter (EKF) for online estimation of fully physically consistent time-varying inertial parameters. These estimates are fed through to the WB-MPC's full rigid body dynamics model, resulting in more accurate feedforward torques for task demands under external load. Additionally, a virtual reality (VR) interface is provided that enables an operator to provide task-specific references without regard to payload while delegating dynamic balance to the WB-MPC. The framework is validated with simulation and hardware experiments on a humanoid system.
作者：James Foster, Beomyeong Park, Luigi Penco, Robert J. Griffin
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