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LLMs and symbolic reasoning combine for multi-robot planning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PLANTOR, a framework that combines large language models (LLMs) with symbolic reasoning for multi-robot temporal planning. The system uses LLMs to create structured Prolog knowledge bases from natural language task descriptions, which are then checked for errors and used to generate symbolic plans. These plans are refined into robot actions and optimized for timing before being converted into executable behavior trees. While LLM-generated knowledge bases can significantly reduce manual effort, the paper suggests that human correction may still be necessary for critical tasks, advocating for a hybrid approach where LLMs assist in creating symbolic artifacts while planning remains inspectable. AI

IMPACT This research could streamline the development of complex robotic systems by automating knowledge base creation and planning, potentially accelerating the deployment of multi-robot applications.

RANK_REASON Research paper detailing a novel framework for AI-assisted robotics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs and symbolic reasoning combine for multi-robot planning

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Research paper detailing a novel framework for AI-assisted robotics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Enrico Saccon, Matteo Saveriano, Edoardo Lamon, Luigi Palopoli, Marco Roveri ·

    Combining Large Language Models and Symbolic Reasoning for Multi-Robot Temporal Planning through Explainable Knowledge Bases

    arXiv:2502.19135v2 Announce Type: replace Abstract: We present PLANTOR, a framework for generating and executing multi-robot task plans from natural-language task descriptions through LLM-assisted knowledge-base construction. The approach uses large language models to synthesize …

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