HULK: Learning Whole-Body Forceful Loco-Manipulation for Humanoids

2026-10-06

AI Summary

This study addresses the challenges of balance maintenance and command tracking in humanoid robots carrying heavy loads, where center-of-mass shifts and sustained payloads induce instability. To this end, a whole-body force-controlled mobile manipulation framework is proposed. Methodologically, model predictive control guides reinforcement learning to train separate teacher policies for dual-arm manipulation and locomotion, which are subsequently distilled into a unified policy. Furthermore, a capture-point-based control barrier function is introduced to augment the wrist-force teacher, thereby enhancing dynamic stability under heavy loads. Experimental results demonstrate that the proposed strategy achieves minimal velocity tracking error under a 10 kg payload, reduces the divergent component of motion (DCM) deviation by 35.7%, and successfully withstands external disturbances up to 130 N applied as torso pushes.

Abstract

Humanoid loco-manipulation of large, heavy objects demands forceful interaction across the entire body. However, such payloads shift a humanoid's center of mass and impose sustained loads across the upper body, challenging balance and command tracking. We present HULK, a whole-body control framework for forceful loco-manipulation. Using model predictive control (MPC) to guide reinforcement learning with predictions of the loaded dynamics, we train two teachers: one tracks arm motions under wrist forces, and the other locomotes while holding large objects against the body. A capture-point control barrier function augments the wrist-force teacher during training to improve balance under load. We distill both teachers into a single policy. Evaluation spans simulation and the Unitree G1. In simulation, the teacher with the barrier function achieves the lowest forward and lateral velocity tracking errors at 10 kg per arm among evaluated controllers and reduces aggregate divergent component of motion (DCM) excursion magnitude by 35.7% relative to MPC-guided reinforcement learning alone. Our wrist-force teacher withstands torso push disturbances of up to 130 N.

Problem

• Humanoid
• Loco-manipulation
• Heavy objects
• Balance
• Command tracking

Innovation

• Whole-Body Control
• Loco-Manipulation
• Model Predictive Control
• Control Barrier Function
• Knowledge Distillation