形态等变流匹配用于双臂移动操作
Morphologically Equivariant Flow Matching for Bimanual Mobile Manipulation
AI总结：
本文提出利用形态对称性作为诱导偏倚，通过流匹配政策提升双臂移动操作的样本效率和零样本泛化能力。
摘要：
移动操作需要协调高维双臂机器人的控制。模仿学习方法广泛用于解决这些机器人任务，但通常忽略此类系统固有的双侧形态对称性。本文论证形态对称性是学习双臂移动操作中被低估但关键的诱导偏倚：在一种配置中解决任务的方法直接决定了其镜像对应任务的解决方法。我们正式化了这种对称性先验，并展示其约束最优双臂策略必须具备双利手性和对称性。我们引入了一个C₂等变流匹配策略，通过正则化训练损失或等变速度网络强制反射对称性。在平面和6自由度移动操作任务中，对称性指导的策略一致提高了样本效率，并实现了零样本泛化到训练分布中没有的镜像配置。我们进一步在真实世界操作任务中验证了这种零样本泛化能力，使用TIAGo++机器人。共同，我们的发现确立了形态对称性作为有效、可推广且可扩展的诱导偏倚，用于双利手生成策略学习。
英文摘要：
Mobile manipulation requires coordinated control of high-dimensional, bimanual robots. Imitation learning methods have been broadly used to solve these robotic tasks, yet typically ignore the bilateral morphological symmetry inherent in such systems. We argue that morphological symmetry is an underexplored but crucial inductive bias for learning in bimanual mobile manipulation: knowing how to solve a task in one configuration directly determines how to solve its mirrored counterpart. In this paper, we formalize this symmetry prior and show that it constrains optimal bimanual policies to be ambidextrous and equivariant under reflections across the robot's sagittal plane. We introduce a $\mathbb{C}_2$-equivariant flow matching policy that enforces reflective symmetry either via a regularized training loss or an equivariant velocity network. Across planar and 6-DoF mobile manipulation tasks, symmetry-informed policies consistently improve sample efficiency and achieve zero-shot generalization to mirrored configurations absent from the training distribution. We further validate this zero-shot generalization capability on a real-world manipulation task with a TIAGo++ robot. Together, our findings establish morphological symmetry as an effective, generalizable, and scalable inductive bias for ambidextrous generative policy learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
