RESEARCH · RESEARCH · #2188
Hierarchical feedback-guided LLM planning for multi-component cyber-physical systems (arXiv:2610.22441v1)
This arXiv v1 paper proposes a hierarchical feedback framework that prompts LLMs to produce a structured task plan plus signal temporal logic (STL) constraints, detects definite contradictions between them, and feeds diagnostic information from failed control synthesis back to the planner. The authors report experiments on seven heterogeneous multi-component CPS scenarios and state a reported success rate of 87.9% when using GPT-5 to generate executable plans and feasible controls that meet the original natural-language missions.
KEY POINTS
- This arXiv v1 paper proposes a hierarchical feedback framework that prompts LLMs to produce a structured task plan plus signal temporal logic (STL) constraints, detects definite contradictions between them, and feeds diagnostic information from failed control synthesis back to the planner.
- The authors report experiments on seven heterogeneous multi-component CPS scenarios and state a reported success rate of 87.9% when using GPT-5 to generate executable plans and feasible controls that meet the original natural-language missions.
- Bridging natural-language LLM plans to provably executable and safe control for CPS with structured constraints and diagnostic feedback addresses a key barrier to deploying LLMs in real-world robotics and control systems.
WHY IT MATTERS
Bridging natural-language LLM plans to provably executable and safe control for CPS with structured constraints and diagnostic feedback addresses a key barrier to deploying LLMs in real-world robotics and control systems.