论文/arXiv 2609.27095·1 个月前

跨不同具身的智能

本文探讨跨具身差异的通用具身智能，主张以具身多样性扩展和广泛学习先验促进迁移，并呼吁更好评测。

AI 整理

• 本体 SDK 与控制
• 评测与安全

原文链接：Intelligence Across Embodiments（arXiv 2609.27095 · 发布日期：2026-09-22）

Intelligence Across Embodiments

本文探讨跨具身差异的通用具身智能，主张以具身多样性扩展和广泛学习先验促进迁移，并呼吁更好评测。

核心要点

• 机器人具身属性随机器人而异并会变化，通用具身智能需跨差异积累学习。
• 现有通过人工设计对应关系弥合具身差异的方法虽有实用收益，但长期迁移范围受限。
• 文章提出将具身多样性作为扩展轴，并以广泛学习先验作为互补要素。
• 作者呼吁更好刻画具身差距与迁移性能的评测，连接异构机器人经验学习与物理智能研究。

英文摘要 (Abstract)

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.

作者：Bo Ai, Henrik I. Christensen, Hao Su

来源

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1. arXiv 2609.27095：Intelligence Across Embodiments访问 2026-10-21