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
JEPA Policy is introduced, a diffusion-free framework that uses the action chunk and its observed future representation as paired training targets as paired training targets and supports paired future-representation supervision supervision as a practical approach to low-latency visuomotor imitation without iterative ge...
Jie Xu, Kang-Jin Yu, Zi-Yi Jin et al.· 0 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,...
NebulaVLA is presented, an asynchronous dual-frequency architecture that decouples high-level semantic reasoning from low-level action control, optimizing computational resources and modularity and introduces GESTURE-7, a unified language-grounded action representation.
Congyu Zhao, Shuai Tian, Xu Zhang et al.· 0 citations
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