OmniDex: Scaling Dexterous Hand Grasping to Diverse Cluttered Scenes
Dexterous grasping is the foundational primitive in embodied AI, demanding massive data to train robust models. As real-world data collection is expensive, simulation has become the mainstream paradigm. Yet, while cluttered scenes best reflect real-world applications, learning to grasp within them is bottlenecked by a critical scarcity of large-scale data. To resolve this, 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 ric...
| Authors | Naiyu Fang, Zhongjin Luo, Yuxin Mo et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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