2 citations · 2 across the 4 of their papers we have counts for
5 papers · 1 filter
SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation
Jingkai Wang, Zihan Tang, Gu Zhang +7
Vision-language-action policies rely on large multimodal backbones to jointly perform perception, language conditioning, and action generation at every control step. Much of this c…
UniDex: A Robot Foundation Suite for Universal Dexterous Hand Control from Egocentric Human Videos
Gu Zhang, Qicheng Xu, Haozhe Zhang +16
Dexterous manipulation remains challenging due to the cost of collecting real-robot teleoperation data, the heterogeneity of hand embodiments, and the high dimensionality of contro…
Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning
Zhecheng Yuan, Tianming Wei, Shuiqi Cheng +3
Can we endow visuomotor robots with generalization capabilities to operate in diverse open-world scenarios? In this paper, we propose \textbf{Maniwhere}, a generalizable framework…
3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations
Yanjie Ze, Gu Zhang, Kangning Zhang +3
Imitation learning provides an efficient way to teach robots dexterous skills; however, learning complex skills robustly and generalizablely usually consumes large amounts of human…
Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation
Yuanchen Ju, Kaizhe Hu, Guowei Zhang +3
Enabling robotic manipulation that generalizes to out-of-distribution scenes is a crucial step toward open-world embodied intelligence. For human beings, this ability is rooted in…