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

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.