Finding the object referred to by language in a partially observed 3D scene is a core capability for embodied agents. Existing approaches either couple object search with online exploration, which can be costly when relevant observations have already been captured, or query pre-built open-vocabulary maps and scene grap...
Shi-Xiong Xu, Zhi-Yuan Chen, Song Ding et al.· 0 citations
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
SegDiff, a closed-loop visuomotor policy that decomposes demonstrations into motion segments between keyposes and learns to predict the continuous trajectory from the current state to the next keypose, enabling long-horizon prediction with real-time refinement.
Hai-Dong Cao, Wenjun Cao, Quanhao Li et al.· arXiv.org· 0 citations
FM-VLA is proposed, a VLA model with force-based memory, enabling temporal context reasoning for non-Markovian, contact-rich manipulation, and achieves over 80% success rate with minimal inference overhead, significantly outperforming baseline approaches.
Ruicheng Li, Qi-Xiu Li, Ruichun Ma et al.· arXiv.org· 1 citation
This work introduces HandWorld, a unified generative framework that focuses on hand-object interaction and jointly models ego-centric videos and hand actions and learns shared cross-domain conditions through a dual-branch condition network that integrates information from both video and action domains.
Zhihao Sun, Zhi-Ying Du, Xitong Yang et al.· 1 citation
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