DexFormer: 通过历史条件化的Transformer实现跨具身体验的灵巧操作
DexFormer: Cross-Embodied Dexterous Manipulation via History-Conditioned Transformer
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
- Wuhan University（武汉大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
DexFormer通过历史条件化的Transformer实现跨具身体验的灵巧操作，能够适应不同手型并实现零样本迁移。
AI中文摘要：
灵巧操作仍然是机器人领域中最具有挑战性的问题之一，需要在复杂的、接触丰富的动态环境下对高自由度的手和臂进行协调控制。主要障碍是具身体验的多样性：不同灵巧手表现出不同的运动学和动力学，迫使先前的方法训练单独的策略或依赖于具有每个具身体验解码器头的共享动作空间。我们提出了DexFormer，一个基于修改后的Transformer主干的端到端、动态感知的跨具身体验策略，其条件于历史观察。通过利用时间上下文来实时推断形态和动力学，DexFormer适应于多样化的手配置并产生适合具身体验的控制动作。在各种程序生成的灵巧手资产上训练，DexFormer获得了一种可推广的操作先验，并在Leap手、Allegro手和Rapid手上表现出强大的零样本迁移能力。我们的结果表明，单一策略可以跨异质手具身体验进行泛化，建立了一个可扩展的基础，用于跨具身体验的灵巧操作。项目网站：https://davidlxu.github.io/DexFormer-web/。
英文摘要：
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/.
