General-purpose embodied intelligence requires robots to coordinate diverse skills over long horizons, track task progress, and recover from failures. Specialist policies alone do not ensure task-level reliability, while powerful multimodal agents rely on costly reasoning and external experience that compact models str...
Xiaomi Embodied Intelligence Team Yuechen Luo, Chong He, Fang Li et al.· 0 citations
Reinforcement learning for multi-turn search reasoning typically relies on terminal outcome rewards, which cannot distinguish useful, redundant, and harmful intermediate interactions. We propose LOTAPO , a self-generated process-supervision method based on backward leave-one-turn attribution. For each search turn, LOTA...
UCAG-P is introduced, a camera-centric unified action formulation that structurally aligns heterogeneous embodied datasets into a shared geometric action space, and allows a shared VLA policy to learn transferable manipulation geometry while retaining embodiment-specific controllability.
Xiaomi Embodied Intelligence Team, University of Macau Shaoqing Xu, Fang Li et al.· 0 citations
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