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
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,...
State2State is proposed, an environment-derived mid-training method that converts explored environment states into training objectives, challenging agents to reach a specified target state by deriving tasks from environment exploration and verifying success through rule-based state matching.
Xuanyu Lei, Yi-Qi Zhu, Chen-Liang Li et al.· 1 citation
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