A Temporal Planning Framework for Multi-Agent Systems via LLM-Aided Knowledge Base Management

TL;DR

This paper introduces PLANTOR, a framework combining LLMs and Prolog for multi-robot planning, enabling flexible, explainable, and scalable task execution with formal correctness.

Contribution

The paper presents a novel integration of LLMs with Prolog-based planning for multi-robot systems, enhancing flexibility and explainability.

Findings

LLMs can generate accurate knowledge bases with minimal human feedback.

Prolog ensures formal correctness and explainability of plans.

The framework successfully applied to multi-robot assembly tasks.

Abstract

This paper presents a novel framework, called PLANTOR (PLanning with Natural language for Task-Oriented Robots), that integrates Large Language Models (LLMs) with Prolog-based knowledge management and planning for multi-robot tasks. The system employs a two-phase generation of a robot-oriented knowledge base, ensuring reusability and compositional reasoning, as well as a three-step planning procedure that handles temporal dependencies, resource constraints, and parallel task execution via mixed-integer linear programming. The final plan is converted into a Behaviour Tree for direct use in ROS2. We tested the framework in multi-robot assembly tasks within a block world and an arch-building scenario. Results demonstrate that LLMs can produce accurate knowledge bases with modest human feedback, while Prolog guarantees formal correctness and explainability. This approach underscores the potential of LLM integration for advanced robotics tasks requiring flexible, scalable, and human-understandable planning.