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

An Dang, Arturo Flores Alvarez, Yu-Ming Chen, Conor Mc Gartoll, Helen Sun, Aaron Ames, Nima Fazeli, Manikantan Nambi · N/A · 2026

Summary

Abstract Summary

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.

Key Points

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.

Methodology

Our goal is to learn a single whole-body controller for payload-driven loco-manipulation: a policy that follows planar velocity, base height, torso orientation and arm joint commands while a load acts anywhere on the upper body, and that transfers to hardware from proprioception alone.

We train two specialist teachers separately, one that tracks arm poses under forces applied at the wrists and one that locomotes while maintaining whole-body grasps across multiple objects. We distill the two into the single student we deploy ( Sec. III-G , Fig.

Methodology — HULK: Learning Whole-Body Forceful Loco-Manipulation for Humanoids — Fig. 4: CP-CBF guidance. The ghost illustrates a recovery step into a separate admissible footprint; the dashed boundary marks $h_{\mathrm{cp}}=0$. The inset shows command correction and reward feedback during training only.

Experiments

We evaluate whether HULK can maintain commanded motion under sustained load, recover from disturbances, and transfer these capabilities to forceful loco-manipulation tasks. Controllers and comparisons. We compare against FALCON, Thor, and AMO [ 15 , 16 , 28 ] .

The simulation rows Ours and Ours + CP-CBF evaluate the wrist-force teachers; hardware trials evaluate the distilled student deployed zero-shot on the Unitree G1. In simulation, all controllers receive identical commands and perturbation schedules on the same platform.

Experiments — HULK: Learning Whole-Body Forceful Loco-Manipulation for Humanoids — Fig. 5: Real-world tasks. Cart push/pull, payload carry, fridge carry, and canister carry on the Unitree G1. Together they span the upper body: force acts at the wrists in the first two and against the torso in the last two, and every task is performed while walking.

Sources and Demonstrations

Figures are reproduced from Dang et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.