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This research addresses a critical bottleneck in embodied AI by enabling quadrupedal robots like the Unitree Go2 to instantly adapt their control policies when hardware degrades or payloads shift, such as during injury simulation. By inferring physical changes from short interaction histories within half a second, the proposed framework allows learning-based agents to maintain stable locomotion where non-adaptive methods fail. This work advances the field of robotics by demonstrating that explicit online embodiment identification is essential for deploying robust AI in real-world environments with uncertain hardware conditions.
Rapid Embodiment Adaptation for Quadrupedal Locomotion
arXiv:2608.01506v1 Announce Type: cross Abstract: Humans readily adapt their movements as their bodies change through aging, injury, or load carrying, but learning-based robot policies often break when hardware properties shift. We introduce an online embodiment adaptation framework for quadrupedal locomotion that infers embodiment parameters from short interaction histories and conditions control on the inferred hardware state. Our method pairs a generalist policy trained under embodiment randomization with a lightweight adaptation module that identifies physical changes within half a second. We evaluate two representative forms of embodiment variation: joint-range constraints and trunk-mass changes, corresponding to joint-level kinematic degradation and body-level dynamic variation. In simulation, the module accurately estimates these changes and enables closed-loop control that substantially outperforms policies conditioned directly on interaction history. On a real Unitree Go2 robot, our system maintains stable locomotion under severe instances of the evaluated changes, including a fully locked leg and a 5 kg payload, where non-adaptive methods fail. These results demonstrate the practicality of explicit online embodiment identification for rapid adaptation to joint-limit and payload-mass changes, and provide a step toward handling broader forms of uncertain, degraded, or changing robot hardware.
Abstract:Humans readily adapt their movements as their bodies change through aging, injury, or load carrying, but learning-based robot policies often break when hardware properties shift. We introduce an online embodiment adaptation framework for quadrupedal locomotion that infers embodiment parameters from short interaction histories and conditions control on the inferred hardware state. Our method pairs a generalist policy trained under embodiment randomization with a lightweight adaptation module that identifies physical changes within half a second. We evaluate two representative forms of embodiment variation: joint-range constraints and trunk-mass changes, corresponding to joint-level kinematic degradation and body-level dynamic variation. In simulation, the module accurately estimates these changes and enables closed-loop control that substantially outperforms policies conditioned directly on interaction history. On a real Unitree Go2 robot, our system maintains stable locomotion under severe instances of the evaluated changes, including a fully locked leg and a 5 kg payload, where non-adaptive methods fail. These results demonstrate the practicality of explicit online embodiment identification for rapid adaptation to joint-limit and payload-mass changes, and provide a step toward handling broader forms of uncertain, degraded, or changing robot hardware.
Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
I.2.9; I.2.6; I.2.8
arXiv:2608.01506 [cs.RO]
(or arXiv:2608.01506v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.01506 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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