OmniDex: Scaling Dexterous Hand Grasping to Diverse Cluttered Scenes
Furthermore, we introduce the OmniDex model to overcome the grasp multimodality and last-millimeter precision errors plaguing current generative models.
ProofPaper ↗
Key points
- Dexterous grasping is the foundational primitive in embodied AI, demanding massive data to train robust models.
- 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.
- By coupling Soft Winner-Takes-All learning with human-inspired physical constraints during training, and utilizing physics-driven ranking, our approach achieves robust dexterous grasping without the latency of post-optimization.
- Experimental results show that OmniDex model achieves state-of-the-art performance and strong generalization across diverse scenes, views, and unseen objects.
Sources (1)
- [1]OmniDex: Scaling Dexterous Hand Grasping to Diverse Cluttered ScenesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 03:58 AM
Furthermore, we introduce the OmniDex model to overcome the grasp multimodality and last-millimeter precision errors plaguing current generative models.
Dexterous grasping is the foundational primitive in embodied AI, demanding massive data to train robust models.
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