arXiv AI By Gwen Yidou-Weng, Edward Sun, Tianyi Ma, Metin Alp Dogan, Benjie Wang, Allen Peng, Guy Van den Broeck, Yuchen Cui

Meta-Ctrl: Guaranteed Plan Generation by Decoupling Syntactic and Semantic Constraints

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Meta-Ctrl is a constrained‑decoding framework that guarantees syntactic and semantic constraints in robot plans generated by large language models. It introduces meta‑tokens—a compact vocabulary of grounded actions—to enforce syntax at the token level and semantics (preconditions, goals, ordering) at the action level, dramatically reducing memory usage from over 107 TB to under 2 GB. The approach enables a small open‑weight LM to outperform GPT‑4 on subgoal success rates in the WAH‑NL LoTa‑Bench protocol and consistently improves performance on the Embodied Agent Interface, while real‑world tabletop robot experiments show every plan satisfies its preconditions and goals by construction.

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arXiv AI
Aug 25

Physical Agentic AI: An Architecture for Orchestrating a Robot Crew with LLMs

Physical Agentic AI proposes an architecture that links semantic planning with physical execution for robot crews. Each robot exposes a typed skill library, while a foundation model planner decomposes tasks into phases and assigns robot‑skill pairs. A Robot Orchestrator validates and authorizes one skill at a time, ensuring actions are grounded in robot capabilities, system state, and workflow constraints before actuation.

By Xinyuan Liu, Eren Sadikoglu, Riana Chatterjee, Ransalu Senanayake