Inertially Significant Humanoid Teleoperation via Whole Body Model Predictive Control and Online Parameter Estimation

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.

Robotics (cs.RO)

arXiv:2610.22176 [cs.RO]

(or arXiv:2610.22176v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.22176 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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