OmniDex: Scaling Dexterous Hand Grasping to Diverse Cluttere
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: OmniDex: Scaling Dexterous Hand Grasping to Diverse Cluttered Scenes. This paper presents OmniDex, a model that addresses the challenge of dexterous grasping in cluttered environments by curating a large-scale benchmark with over 2.6 million scenes and 0.4B grasp ground truths. The model c…
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Reported quantity · scenes: 2600000 other · Basis not reported
“is, we curate high-quality 3D objects and supporting bases, proposing a scalable seed-and-filter strategy that bypasses sluggish scene-level optimization. This yields an unprecedented benchmark comprising over 2.6 million scenes and 0.4B scene-specific grasp ground truths, featuring diverse realistic layouts paired with rich semantic and geometric observations. Furthermore, we introduce the OmniDex model to overcome”
Exact source · revision 1Source owner not reported
Reported quantity · grasp ground truths: 400000000 other · Basis not reported
“is, we curate high-quality 3D objects and supporting bases, proposing a scalable seed-and-filter strategy that bypasses sluggish scene-level optimization. This yields an unprecedented benchmark comprising over 2.6 million scenes and 0.4B scene-specific grasp ground truths, featuring diverse realistic layouts paired with rich semantic and geometric observations. Furthermore, we introduce the OmniDex model to overcome”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsOmniDex: Scaling Dexterous Hand Grasping to Diverse Cluttered Scenes
This paper presents OmniDex, a model that addresses the challenge of dexterous grasping in cluttered environments by curating a large-scale benchmark with over 2.6 million scenes and 0.4B grasp ground truths. The model combines Soft Winner-Takes-All learning with human-inspired physical constraints to achieve robust grasping without post-optimization latency, demonstrating strong generalization across diverse scenes and objects.
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