一个策略通吃多种机械手，靠历史信息实时适配形态差异。
DexFormer: Cross-Embodied Dexterous Manipulation via History-Conditioned Transformer
- 用Transformer结合历史观测，动态推断手的结构和动力学。
- 在多种生成手型上训练，零样本迁移到Leap/Allegro/Rapid手。
- 无需为每种手单独训练，适合多形态机器人协同操作。
灵巧操作是机器人领域最挑战的问题之一，需对高自由度手与臂进行协调控制，应对复杂的接触动力学。主要障碍在于本体差异：不同灵巧手具有不同的运动学和动力学特性，导致以往方法需为每种手单独训练策略或依赖共享动作空间并搭配特定解码头。本文提出DexFormer，一种基于改进Transformer架构的端到端、动力学感知跨本体策略，通过历史观测条件化实现自适应。该模型利用时序上下文在线推断手的形态与动力学，生成适配当前本体的控制动作。在多种程序生成的灵巧手资产上训练后，DexFormer展现出强大的零样本迁移能力，成功应用于Leap Hand、Allegro Hand和Rapid Hand。结果表明，单一策略可泛化至异构手本体，为跨本体灵巧操作提供了可扩展的基础。
原文摘要 · Abstract (English)
Dexterous manipulation remains one of the most challenging problems in robotics, requiring coherent control of high-DoF hands and arms under complex, contact-rich dynamics. A major barrier is embodiment variability: different dexterous hands exhibit distinct kinematics and dynamics, forcing prior methods to train separate policies or rely on shared action spaces with per-embodiment decoder heads. We present DexFormer, an end-to-end, dynamics-aware cross-embodiment policy built on a modified transformer backbone that conditions on historical observations. By using temporal context to infer morphology and dynamics on the fly, DexFormer adapts to diverse hand configurations and produces embodiment-appropriate control actions. Trained over a variety of procedurally generated dexterous-hand assets, DexFormer acquires a generalizable manipulation prior and exhibits strong zero-shot transfer to Leap Hand, Allegro Hand, and Rapid Hand. Our results show that a single policy can generalize across heterogeneous hand embodiments, establishing a scalable foundation for cross-embodiment dexterous manipulation. Project website: https://davidlxu.github.io/DexFormer-web/.
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