Reinforcement learning for embodied control remains constrained by the difficulty of reward specification. Although recent large language model (LLM)-based methods can synthesize reward functions from natural-language descriptions, they often fail to capture subtle behavioral properties that humans care about, such as...
Eren Sadikoglu, Aditya Taparia, Xin-Yuan Liu et al.· 0 citations
China’s iron and steel sector is pivotal to global industrial decarbonization, yet near-zero transition pathways under heterogeneous regional resource endowments remain poorly understood. Here we develop a plant-resolved, spatially explicit framework that integrates a facility-level emission database, a cost-minimizing...
Yan Yan, Xin-Yuan Liu, Hancheng Dai et al.· Nature Communications· 0 citations
Reliable video world models could provide scalable predictive environments for robot learning, planning, and evaluation. However, generated robot videos can violate physical principles and complete tasks through physically implausible behavior, limiting their reliability for robot learning and planning. Current video-g...
Isaiah Milkey, Som Sagar, Aditya Taparia et al.· 0 citations
This paper introduces Physical Agentic AI, a framework for skill-grounded robot agent orchestration, in which each robot exposes a typed library of executable skills while a foundation model planner decomposes a task into phases and assigns each phase to a robot-skill pair.
Xin-Yuan Liu, Eren Sadikoglu, R. Chatterjee et al.· 0 citations
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