Author

Hongsheng Li

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Preprint Oct 2026

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...

N. Fang, Zhong-Jin Luo, Yu-Xin Mo et al. · 0 citations

Preprint Aug 2026

ACE-Ego-Hand: Repurposing Video Diffusion Models for Occlusion-Robust Egocentric 3D Hand Motion Recovery

ACE-Ego-Hand is introduced, an offline clip-level framework that extracts features via a Deterministic Clean-Latent Encoder and decodes them with a Bidirectional Spatiotemporal Decoder, offering a scalable path from everyday human video to robot manipulation data.

Yu-Fei Liu, Xixi Wang, Hao Li et al. · 3 citations · ⚡2

Preprint Aug 2026

DreamHand: Repurposing Video Diffusion Models for Occlusion-Robust Egocentric 3D Hand Motion Recovery

DreamHand is introduced, an offline clip-level framework that extracts features via a Deterministic Clean-Latent Encoder and decodes them with a Bidirectional Spatiotemporal Decoder, offering a scalable path from everyday human video to robot manipulation data.

Yufei Liu, Xixi Wang, Hao Li et al. · 0 citations