发表机构
Edison Academy Magnet School; South Brunswick High School; Princeton University（爱迪生学院磁石学校; 南布朗斯维克高中; 普林斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对执行器退化下的四足强化学习问题，提出将Unitree Go1建模为含肢体和身体层级秩-2单元的胞复形并采用Hodge消息传递的节点-边-面Hodge策略，实验显示其在未见过的退化场景中表现最优，验证了高阶形态作为有效归纳偏置的价值。
AI 中文摘要
执行器退化使四足运动变为需要关节补偿丧失驱动能力的协调问题。现有研究表明，感知形态的图策略可提升身体扰动下的学习与泛化能力。本文探究能否通过显式建模高阶机械结构强化这些优势：将Unitree Go1表示为含肢体和身体层级秩-2单元的胞复形，并应用基于Hodge的消息传递。在退化训练下，节点-边-面Hodge策略在未见过的执行器退化场景中实现最高回报，具备更高存活率和更低速度跟踪误差。这些结果支持高阶形态作为执行器退化下全身补偿的有效归纳偏置。
英文摘要
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.
CommentsAccepted to IROS Workshop BLPC 2026
