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DexDiffuser: Generating Dexterous Grasps with Diffusion Models

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arxiv 2402.02989 v3 pith:BFQJDP7Q submitted 2024-02-05 cs.RO cs.LG

Zehang Weng , Haofei Lu , Danica Kragic , Jens Lundell This is my paper · ORCID
classification cs.ROcs.LG
keywords dexdiffusergraspgraspsdexterouscloudsdexsamplerdiffusiongenerates
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce DexDiffuser, a novel dexterous grasping method that generates, evaluates, and refines grasps on partial object point clouds. DexDiffuser includes the conditional diffusion-based grasp sampler DexSampler and the dexterous grasp evaluator DexEvaluator. DexSampler generates high-quality grasps conditioned on object point clouds by iterative denoising of randomly sampled grasps. We also introduce two grasp refinement strategies: Evaluator-Guided Diffusion (EGD) and Evaluator-based Sampling Refinement (ESR). The experiment results demonstrate that DexDiffuser consistently outperforms the state-of-the-art multi-finger grasp generation method FFHNet with an, on average, 9.12% and 19.44% higher grasp success rate in simulation and real robot experiments, respectively. Supplementary materials are available at https://yulihn.github.io/DexDiffuser_page/

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Cited by 6 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. DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulation

    cs.RO 2024-11 conditional novelty 6.0 of 10

    A dual-phase diffusion planner with dynamics-consistency and LLM-written guidance achieves strong success on goal-adaptive dexterous manipulation in simulation.

  3. Bimanual Grasp Synthesis for Dexterous Robot Hands

    cs.RO 2024-11 conditional novelty 6.0 of 10

    BimanGrasp produces a large-scale simulated dataset of bimanual dexterous grasps and a diffusion model that synthesizes them at quasi-real-time speeds.

  4. DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving

    cs.CV 2024-11 conditional novelty 6.0 of 10

    DiffusionDrive shows that seeding a diffusion policy with K-Means anchor trajectories and truncating the diffusion schedule allows real-time (45 FPS) end-to-end driving planning with a 2-step denoising process and a 8...

  5. AdaDexGrasp: Adaptive Dexterous Grasping via 3D Visuo-Tactile Representation Fusion

    cs.RO 2026-08 conditional novelty 5.0 of 10

    AdaDexGrasp learns to fuse point clouds with finger-level tactile labels to generate, judge, and correct dexterous grasps, reporting 91%/82%/83% success on seen, unseen-object, and unseen-category sets in simulation.

  6. GeoMatch++: Morphology Conditioned Geometry Matching for Multi-Embodiment Grasping

    cs.RO 2024-12 conditional novelty 5.0 of 10

    Adding explicit robot morphology features via graph attention raises average out-of-domain grasp success by 9.64 percentage points over GenDexGrasp, but the gain is uneven, lacks error bars, and is reported for only 3...

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