In-context robot learning enables robots to acquire new behaviors from demonstrations without test-time parameter updates. However, visual and kinematic specifications alone may not distinguish contact-rich behaviors that share similar motions but require different interaction intensities or patterns. We propose Contac...
Xiang-Li Nie, Yujie Zang, Yu-Hang Zheng et al.· 0 citations
GIFT (Guided Intermediate Feature Training), an architecture-flexible framework for learning intermediate features that translates these structures into training-time constraints through geometry alignment, affordance prediction, and goal-region reconstruction, establishes learning functionally structured intermediate...
Yu-Peng Zheng, Xiang Li, Song-En Gu et al.· 2 citations
WALA, a framework for learning executable latent actions from both action-labeled demonstrations and action-free videos, achieves strong performance on RoboTwin, sets a new state-of-the-art result on RoboCasa, and improves both policy performance and generalization in real-world manipulation tasks.
World action models (WAMs) improve robot control by modeling how observations evolve, but generating future observations at test time incurs substantial latency. Fast-WAM removes this process for efficiency; however, our matched implementations show lower generalization for Fast-WAM than for future-aware alternatives,...
