H2-COMPACT: Human-Humanoid Co-Manipulation via Adaptive Contact Trajectory Policies

TL;DR

This paper introduces a hierarchical learning framework enabling a humanoid robot to cooperatively carry loads with a human partner using only haptic cues, combining intent inference with adaptive legged locomotion.

Contribution

It presents a novel hierarchical policy-learning approach that decouples human intent interpretation from legged movement control, validated in simulation and real-world experiments.

Findings

Humanoid successfully cooperates with humans in load-carrying tasks.

The method achieves performance comparable to a blindfolded human follower.

The framework adapts to different payloads and friction conditions.

Abstract

We present a hierarchical policy-learning framework that enables a legged humanoid to cooperatively carry extended loads with a human partner using only haptic cues for intent inference. At the upper tier, a lightweight behavior-cloning network consumes six-axis force/torque streams from dual wrist-mounted sensors and outputs whole-body planar velocity commands that capture the leader's applied forces. At the lower tier, a deep-reinforcement-learning policy, trained under randomized payloads (0-3 kg) and friction conditions in Isaac Gym and validated in MuJoCo and on a real Unitree G1, maps these high-level twists to stable, under-load joint trajectories. By decoupling intent interpretation (force -> velocity) from legged locomotion (velocity -> joints), our method combines intuitive responsiveness to human inputs with robust, load-adaptive walking. We collect training data without motion-capture or markers, only synchronized RGB video and F/T readings, employing SAM2 and WHAM to extract 3D human pose and velocity. In real-world trials, our humanoid achieves cooperative carry-and-move performance (completion time, trajectory deviation, velocity synchrony, and follower-force) on par with a blindfolded human-follower baseline. This work is the first to demonstrate learned haptic guidance fused with full-body legged control for fluid human-humanoid co-manipulation. Code and videos are available on the H2-COMPACT website.