发表机构
Stanford University; University of California San Diego; Sudo AI GmbH（斯坦福大学; 加州大学圣地亚哥分校; Sudo人工智能有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对机器人具身差异阻碍通用智能的问题，提出以具身多样性为扩展维度、学习广泛先验以支持跨具身迁移，并呼吁改进评估方法。
AI 中文摘要
机器人具身涵盖了智能体与物理世界交互所依赖的感知、运动学、动力学、几何、驱动与控制等属性。这些属性在不同机器人之间存在差异，并随时间变化。我们认为，通用具身智能需要跨这些差异进行累积学习。当前通过设计对应关系来桥接具身差异的主流方法虽能带来立竿见影的实践收益，但其假设限制了长期迁移的适用范围。相反，更通用的方法应发现能随经验增长而支持迁移到更广泛具身的表征。我们提出具身多样性作为有前景的扩展维度，并识别广泛学习的先验作为补充要素。我们呼吁开展能更好刻画具身差距与迁移性能的评估。更广泛地，跨具身学习将异构机器人经验学习的实践挑战与受自然启发的更广阔科学追求——即适应并与其具身共同进化以获得对其行为与物理形态自主性的物理智能——联系起来。
英文摘要
Robotic embodiment encompasses the sensing, kinematics, dynamics, geometry, actuation, and control through which an agent physically interacts with the world. These properties vary across robots and change over time. We argue that general embodied intelligence requires learning that accumulates across these differences. Prevailing methods that engineer correspondences to bridge embodiment differences offer immediate practical gains, but their assumptions limit the scope of transfer in the long run. Instead, a more general approach should discover representations that support transfer to a larger range of embodiments as experience grows. We propose embodiment diversity as a promising axis of scaling, and identify broad learned priors as a complementary ingredient. We call for evaluations that better characterize embodiment gaps and transfer performance. More broadly, cross-embodiment learning connects the practical challenge of learning from heterogeneous robot experience with a broader scientific pursuit inspired by nature - physical intelligence that adapts and co-evolves with its embodiments to gain agency over its behavior and physical forms.
CommentsAccepted to the International Symposium of Robotics Research (ISRR) 2026
