用大模型+遗传算法自动生成机器人任务规划树
Automatic Robot Task Planning by Integrating Large Language Model with Genetic Programming
- 结合大语言模型与遗传编程生成行为树
- 输入自然语言指令，输出可执行的可靠任务计划
- 适合无人系统开发人员快速构建智能控制逻辑
准确的任务规划对自主系统（如机器人、无人机、自动驾驶车辆）的控制至关重要。行为树（BTs）因其模块化、灵活性和可复用性，被认为是任务规划中最具代表性的控制策略定义框架。然而，为机器人系统生成可靠且准确的基于行为树的控制策略仍具挑战性，通常需要领域专业知识。本文提出LLM-GP-BT方法，利用大语言模型（LLM）与遗传编程（GP）自动化生成和配置行为树。该方法可处理以人类自然语言表达的机器人任务指令，并以计算高效且用户友好的方式转化为准确可靠的基于行为树的任务计划。所提方法通过仿真实验系统地开发与验证，展示了其在简化自主系统任务规划方面的潜力。
原文摘要 · Abstract (English)
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.
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