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
Agent harnesses specify the roles, instructions, tools, and communication structure used to solve a task, and the right harness depends on the query. Because the value of each design choice is observable only through execution, tailoring a harness to each query has required either executing alternatives at inference ti...
Som Sagar, Sha-Sha Li, He-Jie Cui et al.· 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
ARC (Agentic Resource&Configuration learner), a lightweight hierarchical policy that dynamically selects query-specific agent configurations, consistently improves over budget-matched tool-augmented LLMs, demonstrating that learning per-query agent configurations is a powerful alternative to"one size fits all"designs.
Aditya Taparia, Som Sagar, Ransalu Senanayake· arXiv.org· 2 citations
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