Automatic Robot Task Planning by Integrating Large Language Model with Genetic Programming

TL;DR

This paper introduces LLM-GP-BT, a novel method combining large language models and genetic programming to automate and improve the generation of behavior tree-based task plans for autonomous robots from natural language commands.

Contribution

The paper presents a new integrated approach that automates behavior tree generation using LLMs and genetic programming, reducing reliance on domain expertise and enhancing efficiency.

Findings

Successfully converts natural language commands into behavior trees

Demonstrates computational efficiency in simulation experiments

Shows potential to streamline autonomous system task planning

Abstract

Accurate task planning is critical for controlling autonomous systems, such as robots, drones, and self-driving vehicles. Behavior Trees (BTs) are considered one of the most prominent control-policy-defining frameworks in task planning, due to their modularity, flexibility, and reusability. Generating reliable and accurate BT-based control policies for robotic systems remains challenging and often requires domain expertise. In this paper, we present the LLM-GP-BT technique that leverages the Large Language Model (LLM) and Genetic Programming (GP) to automate the generation and configuration of BTs. The LLM-GP-BT technique processes robot task commands expressed in human natural language and converts them into accurate and reliable BT-based task plans in a computationally efficient and user-friendly manner. The proposed technique is systematically developed and validated through simulation experiments, demonstrating its potential to streamline task planning for autonomous systems.