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DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness

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arxiv 2503.08257 v2 pith:DTL7Y26W submitted 2025-03-11 cs.CV cs.AIcs.RO

Yiming Zhong , Qi Jiang , Jingyi Yu , Yuexin Ma This is my paper · ORCID
classification cs.CVcs.AIcs.RO
keywords graspingdexterousanythingdatasetdexgraspmethodobjectsposes
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A dexterous hand capable of grasping any object is essential for the development of general-purpose embodied intelligent robots. However, due to the high degree of freedom in dexterous hands and the vast diversity of objects, generating high-quality, usable grasping poses in a robust manner is a significant challenge. In this paper, we introduce DexGrasp Anything, a method that effectively integrates physical constraints into both the training and sampling phases of a diffusion-based generative model, achieving state-of-the-art performance across nearly all open datasets. Additionally, we present a new dexterous grasping dataset containing over 3.4 million diverse grasping poses for more than 15k different objects, demonstrating its potential to advance universal dexterous grasping. The code of our method and our dataset will be publicly released soon.

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Cited by 3 Pith papers

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

  1. MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping

    cs.RO 2026-08 conditional novelty 7.0 of 10

    MANGO-Grasp uses geometry-oriented 3D Gaussians and Mahalanobis fields to achieve strong cross-embodiment dexterous grasping, with zero-shot transfer to an unseen hand at 84% simulation and 86% real-world success.

  2. ClutterDexGrasp: A Sim-to-Real System for General Dexterous Grasping in Cluttered Scenes

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A simulation-trained teacher-student policy achieves zero-shot sim-to-real closed-loop target-oriented dexterous grasping in cluttered scenes, with 83.9 percent real-world success.

  3. A Survey: Learning Embodied Intelligence from Physical Simulators and World Models

    cs.RO 2025-07 conditional novelty 4.0 of 10

    Embodied intelligence learning is reviewed through the complementary lenses of physical simulators and world models, with a proposed IR-L0 to IR-L4 robot capability taxonomy.

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