Physically Consistent Online Inertial Adaptation for Humanoid Loco-manipulation

TL;DR

This paper introduces an online inertial adaptation framework for humanoid robots to perform loco-manipulation tasks under significant external loads, combining a physically consistent Kalman filter with whole-body control, validated in simulation and hardware.

Contribution

It presents a novel online estimation and control method that accounts for external loads, improving humanoid robot manipulation and locomotion robustness.

Findings

Successful simulation and hardware validation with external loads

Enhanced inertial parameter estimation accuracy

Improved loco-manipulation capabilities under load

Abstract

The ability to accomplish manipulation and locomotion tasks in the presence of significant time-varying external loads is a remarkable skill of humans that has yet to be replicated convincingly by humanoid robots. Such an ability will be a key requirement in the environments we envision deploying our robots: dull, dirty, and dangerous. External loads constitute a large model bias, which is typically unaccounted for. In this work, we enable our humanoid robot to engage in loco-manipulation tasks in the presence of significant model bias due to external loads. We propose an online estimation and control framework involving the combination of a physically consistent extended Kalman filter for inertial parameter estimation coupled to a whole-body controller. We showcase our results both in simulation and in hardware, where weights are mounted on Nadia's wrist links as a proxy for engaging in tasks where large external loads are applied to the robot.