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UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning

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arxiv 2304.00464 v2 pith:TCLRRFSS submitted 2023-04-02 cs.RO cs.CV

Weikang Wan , Haoran Geng , Yun Liu , Zikang Shan , Yaodong Yang , Li Yi , He Wang This is my paper · ORCID
classification cs.ROcs.CV
keywords learningpolicydexterousgeometry-awaregraspingobjectunidexgraspcurriculum
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose a novel, object-agnostic method for learning a universal policy for dexterous object grasping from realistic point cloud observations and proprioceptive information under a table-top setting, namely UniDexGrasp++. To address the challenge of learning the vision-based policy across thousands of object instances, we propose Geometry-aware Curriculum Learning (GeoCurriculum) and Geometry-aware iterative Generalist-Specialist Learning (GiGSL) which leverage the geometry feature of the task and significantly improve the generalizability. With our proposed techniques, our final policy shows universal dexterous grasping on thousands of object instances with 85.4% and 78.2% success rate on the train set and test set which outperforms the state-of-the-art baseline UniDexGrasp by 11.7% and 11.3%, respectively.

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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. Task-Oriented Human Grasp Synthesis via Context- and Task-Aware Diffusers

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A two-stage diffusion framework that learns task-aware contact maps from initial and goal scene point clouds generates human grasps that avoid collisions and complete Placing, Stacking, and Shelving tasks.

  3. COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual Occluded Grasping

    cs.RO 2025-02 conditional novelty 6.0 of 10

    COMBO-Grasp trains a stabilizing constraint policy and an RL grasping policy, then refines the constraint pose with value-function gradients, improving bimanual grasping of occluded objects in simulation and real world.

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