Correcting robot manipulation policies through human intervention holds great promise for real-world deployment, yet human operators are inherently imperfect in both the actions they provide and the timing of their intervention signals. While the former has been extensively discussed in reinforcement learning (RL), the...
Xin-Yi Zhang, Yi-Nuo Zhao, Pei Ren et al.· 1 citation
World Action Models (WAMs) are becoming increasingly important and useful for embodied intelligence, as they enable robots to anticipate the consequences of candidate actions before interacting with the physical environment. However, underwater robots are usually subject to passive dynamics, such as inertia, buoyancy,...
Cun-Hao Zhu, Yi-Feng Wang, Dong-Liang Xu et al.· 0 citations
A full-spectrum human context taxonomy is introduced that integrates six interconnected levels by viewing humans as observable subjects through visual appearance and spatial geometry, as dynamic actors through kinematic dynamics and interaction modeling, and as situated agents through world simulation and embodied agen...
Yang Chen, Tianqi Wang, Xiao-Wen Jiang et al.· 0 citations
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