DexFormer: Cross-Embodied Dexterous Manipulation via History-Conditioned Transformer

• URL: http://arxiv.org/abs/2602.08278v1
• Date: Mon, 09 Feb 2026 05:16:48 GMT
• Title: DexFormer: Cross-Embodied Dexterous Manipulation via History-Conditioned Transformer
• Abstract summary: Dexterous manipulation remains one of the most challenging problems in robotics.<n>We present DexFormer, an end-to-end, dynamics-aware cross-embodiment policy.<n>Our results show that a single policy can generalize across heterogeneous hand embodiments.
• Score: 18.11650194615062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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