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
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
This work introduces Skill Self-Play (Skill-SP), a co-evolutionary framework comprising a proposer, a solver, and a dynamic skill controller that effectively bridges the gap between structured verification and open-ended exploration.
Siyuan Huang, Pengyu Cheng, Haotian Liu et al.· arXiv.org· 2 citations
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
