Executing an unfamiliar laboratory protocol requires a robot to identify missing capabilities and determine whether each operation remains valid as the experiment progresses. This letter presents BioAgent, an embodied agent that addresses these coupled challenges of capability sufficiency and experimental validity. It...
Gao-Jing Zhang, Chang-Wen Chen, Zi-Chen Luo et al.· 0 citations
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
Diffusion models (DMs) have steadily demonstrated outstanding performance in high-fidelity, controllable generation across diverse scenarios, following the "pretraining-finetuning" paradigm established in LLMs. Despite these advances, conventional DMs still face critical limitations in sophisticated reasoning, autonomo...
Zhi-Yuan Ma, Jia-Bao Wei, Tian-Yu Wang et al.· IEEE Transactions on Pattern...· 0 citations
Generative models, such as diffusion and flow-based models, have shown strong promise for robot policy learning by capturing complex and multimodal action distributions from demonstrations. However, policies trained solely with imitation learning often suffer from imperfect demonstrations and distributional shifts, whi...
Yu Li, Sheng-He Hu, Yu-Han Wang et al.· 0 citations
PACE (Proprioception-Anchored Cross-Modal Encoder) is presented, which supervises temporal visual and F/T representations by predicting proprioceptive state transitions and is robust to perturbations that substantially degrade pose-based and learned-fusion baselines.
Yu-Han Wang, Yurou Chen, Hong-Ye Jiang 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
Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sustained contact. Although simulation-based reinforcement learning offers a scalable training paradigm, discrepancies in visual observations, contact dynamics, and force/torque (F/T...
Yu-Han Wang, Yurou Chen, Hong-Ye Jiang et al.· 0 citations
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