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DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation

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arxiv 2210.02697 v2 pith:WU2W2FYN submitted 2022-10-06 cs.RO cs.CV

Ruicheng Wang , Jialiang Zhang , Jiayi Chen , Yinzhen Xu , Puhao Li , Tengyu Liu , He Wang This is my paper · ORCID
classification cs.ROcs.CV
keywords dexterousdatasetgraspsgraspobjectroboticdexgraspnetgrasping
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Robotic dexterous grasping is the first step to enable human-like dexterous object manipulation and thus a crucial robotic technology. However, dexterous grasping is much more under-explored than object grasping with parallel grippers, partially due to the lack of a large-scale dataset. In this work, we present a large-scale robotic dexterous grasp dataset, DexGraspNet, generated by our proposed highly efficient synthesis method that can be generally applied to any dexterous hand. Our method leverages a deeply accelerated differentiable force closure estimator and thus can efficiently and robustly synthesize stable and diverse grasps on a large scale. We choose ShadowHand and generate 1.32 million grasps for 5355 objects, covering more than 133 object categories and containing more than 200 diverse grasps for each object instance, with all grasps having been validated by the Isaac Gym simulator. Compared to the previous dataset from Liu et al. generated by GraspIt!, our dataset has not only more objects and grasps, but also higher diversity and quality. Via performing cross-dataset experiments, we show that training several algorithms of dexterous grasp synthesis on our dataset significantly outperforms training on the previous one. To access our data and code, including code for human and Allegro grasp synthesis, please visit our project page: https://pku-epic.github.io/DexGraspNet/.

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Cited by 10 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. CoLA-Flow Policy: Temporally Coherent Imitation Learning via Continuous Latent Action Flow Matching for Robotic Manipulation

    cs.RO 2026-01 unverdicted novelty 7.0 of 10

    Flow matching robot policies trained in a continuous latent action space produce smoother, more successful long-horizon manipulation than raw-action-space flow matching, at near-single-step inference speed.

  3. PhotoHOI: Synthesizing 3D Hand-Object Interactions from a Single RGB Photograph

    cs.CV 2026-08 conditional novelty 6.0 of 10

    PhotoHOI turns one RGB photo plus an open-vocabulary instruction into a scene-grounded 3D hand-object motion sequence by parsing the task, recovering objects, planning object motion, and optimizing grasps in a learned...

  4. Grasp Execution Without a Planner: Configuration-Space Grasp Distance Fields with Certified Safety & Guaranteed Quality

    cs.RO 2026-08 reject novelty 6.0 of 10

    Grasp execution via a softmin field over grasp configurations with CBF-QP safety filtering, eliminating trajectory replanning, with a force-closure margin guarantee that fails in one reported trial.

  5. FIRMGrasp: A Friction-Informed Risk Margin for Robust Grasp Synthesis

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A CVaR-based risk-adjusted Ferrari-Canny margin certifies force closure with probability at least β and better ranks adverse-friction grasp success than nominal epsilon.

  6. GraspGraphNet: Graph-Structured Multi-Embodiment Dexterous Grasp Generation

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A single URDF-graph flow-matching model generates executable grasps for Barrett, Allegro, and Shadow hands at 83.48% average success and 40 ms, and reaches 72.70% on finger-removal variants without retraining.

  7. TactiDex: A Real-World Tactile-Guided Benchmark for Human-Like Dexterous Manipulation

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A tactile-rich HOI dataset plus a tri-component force reward improves contact fidelity and success of human-to-robot dexterous transfer over kinematic imitation alone.

  8. InDex: Empowering VLA Models with Intent-Conditioned Arm-Hand Coordination for Dexterous Manipulation

    cs.RO 2026-06 unverdicted novelty 6.0 of 10

    InDex adapts VLA models to high-DoF dexterous manipulation via intent-conditioned fine-tuning and a decoupled diffusion head, outperforming monolithic baselines in simulation tasks with minimal data.

  9. Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A dexterous VLA pretrained on a 2.5M-instance human hand motion dataset transfers skills to a real robot hand, outperforming baselines in manipulation tasks.

  10. CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World

    cs.RO 2025-02 conditional novelty 6.0 of 10

    CordViP achieves strong real-world dexterous manipulation by feeding a diffusion policy with pose-tracked 3D object models and hand point clouds, pretrained on contact maps and arm-hand coordination.

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