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arXiv · 2611.05119

DexHOT: Learning Hand-Object Configuration Transfer for Dexterous Manipulation

Zhewei Gui·Anthony Bolton·Jiyao Zhang·Zeyuan Chen·Qiyang Yan·Tianyu Wang·Hao Dong
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Abstract

Dexterous manipulation involves not only moving objects, but also arranging the hand around them to maintain useful contacts and enable subsequent actions. Yet many learning-based systems specify goals primarily through object poses or trajectories, leaving hand-object configurations to emerge implicitly. We introduce DexHOT, which formulates dexterous manipulation as hand-object configuration transfer: moving between feasible configurations that jointly specify hand articulation and object pose relative to the wrist. DexHOT learns a reusable relation-conditioned transfer primitive from synthetic feasible configuration pairs, without human reference motions, and composes long-horizon behaviors through closed-loop tracking of configuration subgoals. On DexHOTBench with 120 objects, a single policy achieves 80.5% transfer success on seen objects and 69.4% on unseen objects; on a real dexterous hand, it achieves 61.5% success over 200 real-robot transfers and 90% task success across six demonstration-guided tasks. These results show that explicit hand-object goals provide an expressive and composable interface for dexterous manipulation. More details and videos are available at https://dex-hot.github.io/.

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Zhewei Gui, Anthony Bolton, Jiyao Zhang, Zeyuan Chen, Qiyang Yan, Tianyu Wang, Hao Dong. 2026-11-03. DexHOT: Learning Hand-Object Configuration Transfer for Dexterous Manipulation. https://arxiv.org/abs/2611.05119

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