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Found 3 papers, 2 papers with code
Large Language Models to the Rescue: Deadlock Resolution in Multi-Robot Systems
Multi-agent robotic systems are prone to deadlocks in an obstacle environment where the system can get stuck away from its desired location under a smooth low-level control policy.
PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Preference Alignment
Prompt optimization aims to find the best prompt to a large language model (LLM) for a given task.
AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
Rather than using LLMs to directly plan task sub-goals, we instead perform few-shot translation from natural language task descriptions to an intermediate task representation that can then be consumed by a TAMP algorithm to jointly solve the task and motion plan.
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