2026-07-01 04:00 UTCOriginal source2 min readUpdated: 2026-07-01 08:17 UTC

From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation

This research explores leveraging large-scale dexterous grasp datasets to support articulated tool use in robotics. The authors construct a 355k-trajectory pretraining dataset, adopt a hierarchical imitation learning framework, and achieve significant improvements in task success rates in both simulation and real-world experiments.

SourcearXiv RoboticsAuthor: Ying Yuan, Xinyu Liu, Sriram Krishna, David Held

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[Submitted on 29 Jun 2026]

Title:From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation

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Abstract:Large-scale dexterous grasp datasets encode rich priors over hand-object interaction, but their use has largely been confined to grasp generation and pick-and-place manipulation. We study whether such data can instead support functional dexterity in articulated tool use, where a robot must acquire a tool, maintain contact, and operate its functional moving parts. We adapt a hierarchical imitation learning framework that combines high-level hand sub-goal prediction with a low-level goal-conditioned controller. We construct a 355k-trajectory grasp-pretraining dataset from large-scale dexterous grasp annotations and use it to pretrain the low-level controller. The controller is then fine-tuned on downstream task demonstrations. To evaluate this setting, we introduce DexCraft, a simulation benchmark with six articulated tool-use tasks requiring coordinated finger motion. Across simulation and real-world experiments, our approach outperforms end-to-end diffusion policy baselines and hierarchical policies trained from scratch. In the real world, it improves full-task success by 33.3 percentage points over DP3. These results show that grasp datasets can serve not only as resources for grasp synthesis, but also as scalable pretraining data for contact-rich dexterous manipulation. Videos are shown on this https URL .

Comments: Project page: this https URL

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2606.30749 [cs.RO]

(or arXiv:2606.30749v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.30749

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Ying Yuan [view email] [v1] Mon, 29 Jun 2026 18:00:22 UTC (19,478 KB)

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