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SwingBot introduces a novel learning framework that enables high-DoF humanoid robots to perform continuous brachiation using passive wrist hooks, addressing the challenge of long-horizon locomotion in cluttered environments. By leveraging biomimetic keyframes and recurrent state estimation, the system successfully navigates overhead supports without relying on precise relative displacement measurements, demonstrating robustness against payloads and external disturbances. This work advances robotic mobility by integrating complex whole-body dynamics with reinforcement learning techniques suitable for hazardous scenarios.
SwingBot: Learning Whole-Body Brachiation for Humanoid Robots
arXiv:2609.10283v1 Announce Type: new Abstract: Brachiation enables primates to move across overhead supports when ground paths are blocked, suggesting a complementary locomotion mode for robots operating in cluttered or hazardous environments. Bringing this capabil?ity to high-DoF humanoid robots is difficult because the controller must discover a long-horizon release-swing-capture sequence, coordinate alternating contacts with whole-body momentum, and act without reliable measurements of segment?relative displacement or hook-contact state. We present SwingBot, a learning framework for continuous humanoid brachiation with passive wrist hooks. Swing?Bot makes the task trainable by organizing learning around the structure of brachi?ation: biomimetic keyframes make rare release-swing-capture transitions reach?able during early exploration, and recurrent privileged-state estimation provides compact position and contact latents for deployment. Hardware experiments demonstrate continuous bar traversal and robustness to payload, external distur?bances and different bar spacings, showing that this formulation offers a practical route to whole-body robotic brachiation.
Abstract:Brachiation enables primates to move across overhead supports when ground paths are blocked, suggesting a complementary locomotion mode for robots operating in cluttered or hazardous environments. Bringing this capabil?ity to high-DoF humanoid robots is difficult because the controller must discover a long-horizon release-swing-capture sequence, coordinate alternating contacts with whole-body momentum, and act without reliable measurements of segment?relative displacement or hook-contact state. We present SwingBot, a learning framework for continuous humanoid brachiation with passive wrist hooks. Swing?Bot makes the task trainable by organizing learning around the structure of brachi?ation: biomimetic keyframes make rare release-swing-capture transitions reach?able during early exploration, and recurrent privileged-state estimation provides compact position and contact latents for deployment. Hardware experiments demonstrate continuous bar traversal and robustness to payload, external distur?bances and different bar spacings, showing that this formulation offers a practical route to whole-body robotic brachiation.
Robotics (cs.RO)
arXiv:2609.10283 [cs.RO]
(or arXiv:2609.10283v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.10283 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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