Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid

TL;DR

This paper presents a real-time hierarchical inverse dynamics control method for torque-controlled humanoid robots, integrating momentum control and LQR design to enhance robustness and performance during balancing and tracking tasks.

Contribution

It introduces a simplified, real-time implementation of hierarchical inverse dynamics with momentum control and LQR, specifically adapted for torque-controlled humanoids.

Findings

Robust balancing and tracking on humanoids even with disturbances

Effective real-time control on torque-controlled humanoids

Enhanced performance using momentum-based hierarchical inverse dynamics

Abstract

Hierarchical inverse dynamics based on cascades of quadratic programs have been proposed for the control of legged robots. They have important benefits but to the best of our knowledge have never been implemented on a torque controlled humanoid where model inaccuracies, sensor noise and real-time computation requirements can be problematic. Using a reformulation of existing algorithms, we propose a simplification of the problem that allows to achieve real-time control. Momentum-based control is integrated in the task hierarchy and a LQR design approach is used to compute the desired associated closed-loop behavior and improve performance. Extensive experiments on various balancing and tracking tasks show very robust performance in the face of unknown disturbances, even when the humanoid is standing on one foot. Our results demonstrate that hierarchical inverse dynamics together with momentum control can be efficiently used for feedback control under real robot conditions.