[
    {
        "id": "osp-16507",
        "type": "article-journal",
        "title": "Higher-Order Morphology Priors for Quadruped Reinforcement Learning Under Actuator Degradation",
        "author": [
            {
                "family": "You",
                "given": "Derek"
            },
            {
                "family": "Shamsi",
                "given": "Zafir"
            },
            {
                "family": "Wang",
                "given": "Keqin"
            },
            {
                "family": "Allen-Blanchette",
                "given": "Christine"
            }
        ],
        "URL": "https://omanscience.com/en/articles/higher-order-morphology-priors-for-quadruped-reinforcement-learning-under-actuator-degradation",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]