H2-COMPACT: Human-Humanoid Co-Manipulation via Adaptive Contact Trajectory Policies
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909621083439104 |
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| author | Bethala, Geeta Chandra Raju Huang, Hao Pudasaini, Niraj Ali, Abdullah Mohamed Yuan, Shuaihang Wen, Congcong Tzes, Anthony Fang, Yi |
| author_facet | Bethala, Geeta Chandra Raju Huang, Hao Pudasaini, Niraj Ali, Abdullah Mohamed Yuan, Shuaihang Wen, Congcong Tzes, Anthony Fang, Yi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17627 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | H2-COMPACT: Human-Humanoid Co-Manipulation via Adaptive Contact Trajectory Policies Bethala, Geeta Chandra Raju Huang, Hao Pudasaini, Niraj Ali, Abdullah Mohamed Yuan, Shuaihang Wen, Congcong Tzes, Anthony Fang, Yi Robotics 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. |
| title | H2-COMPACT: Human-Humanoid Co-Manipulation via Adaptive Contact Trajectory Policies |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.17627 |