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GenDexGrasp: Generalizable Dexterous Grasping

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arxiv 2210.00722 v2 pith:IDZNHDEV submitted 2022-10-03 cs.RO cs.CV

Puhao Li , Tengyu Liu , Yuyang Li , Yiran Geng , Yixin Zhu , Yaodong Yang , Siyuan Huang This is my paper · ORCID
classification cs.ROcs.CV
keywords gendexgraspgraspingdiversegeneralizableratesuccessartsdexterous
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Generating dexterous grasping has been a long-standing and challenging robotic task. Despite recent progress, existing methods primarily suffer from two issues. First, most prior arts focus on a specific type of robot hand, lacking the generalizable capability of handling unseen ones. Second, prior arts oftentimes fail to rapidly generate diverse grasps with a high success rate. To jointly tackle these challenges with a unified solution, we propose GenDexGrasp, a novel hand-agnostic grasping algorithm for generalizable grasping. GenDexGrasp is trained on our proposed large-scale multi-hand grasping dataset MultiDex synthesized with force closure optimization. By leveraging the contact map as a hand-agnostic intermediate representation, GenDexGrasp efficiently generates diverse and plausible grasping poses with a high success rate and can transfer among diverse multi-fingered robotic hands. Compared with previous methods, GenDexGrasp achieves a three-way trade-off among success rate, inference speed, and diversity. Code is available at https://github.com/tengyu-liu/GenDexGrasp.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DextrAH-RGB: Visuomotor Policies to Grasp Anything with Dexterous Hands

    cs.RO 2024-11 conditional novelty 7.0 of 10

    A dexterous robot hand learns to grasp novel objects from color images alone, trained purely in simulation, and demonstrates competitive real-world performance versus depth-camera policies.

  2. DexVerse: A Modular Benchmark for Multi-Task, Multi-Embodiment Dexterous Manipulation

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A modular benchmark of 100 dexterous manipulation tasks across 3 arms and 6 hands with 3,180 demonstrations reveals that current policies (Diffusion Policy, DP3, OpenVLA, π0.5) achieve only 34% mean success, exposing ...

  3. Grasp to Act: Dexterous Grasping for Tool Use in Dynamic Settings

    cs.RO 2026-02 conditional novelty 6.0 of 10

    Combining wrench-tested grasp optimization with real-time RL finger adjustments lets a 16-DoF robot hand keep tools stable during hammering, sawing, cutting, stirring, and scooping.

  4. AdvGrasp: Adversarial Attacks on Robotic Grasping from a Physical Perspective

    cs.RO 2025-07 conditional novelty 5.0 of 10

    AdvGrasp generates adversarial object deformations that increase gravitational torque and reduce wrench-space stability margins, often degrading robot grasp performance in simulation and on two real objects.

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