Higher-Order Morphology Priors for Quadruped Reinforcement Learning Under Actuator Degradation

Abstract:Actuator degradation turns quadruped locomotion into a coordination problem requiring joints to compensate for lost actuation. Prior work suggests that morphology-aware graph policies improve learning and generalization under body perturbations. We ask whether these benefits can be strengthened by explicitly modeling higher-order mechanical structure. We represent the Unitree Go1 as a cell complex with limb- and body-level rank-2 cells and apply Hodge-based message passing. Under degradation training, the node-edge-face Hodge actor achieves the highest return on unseen actuator degradations, with higher survival and lower velocity-tracking error. These results support higher-order morphology as a useful inductive bias for whole-body compensation under actuator degradation.

Robotics (cs.RO); Machine Learning (cs.LG)

arXiv:2610.10934 [cs.RO]

(or arXiv:2610.10934v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.10934 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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