用大模型自动扩展行为树，让机器人自适应处理新任务和意外情况。
Automatic Behavior Tree Expansion with LLMs for Robotic Manipulation
- 利用大模型动态解析任务规划外的错误，实时扩展行为树。
- 可解决多种任务失败场景，并永久更新策略避免重复出错。
- 适合需要高透明度与可验证性的机器人控制场景。
用于操作任务的机器人系统越来越需要在新任务或不可预测环境中易于配置，同时保持人类可读、可验证的透明策略。本文提出BETR-XP-LLM方法，通过大语言模型动态自动扩展并配置行为树作为机器人控制策略。该方法在任务规划与执行过程中利用大模型解决规划器能力之外的错误。实验表明，该方法能有效应对多种任务与故障情况，并永久更新策略以应对未来类似问题。
原文摘要 · Abstract (English)
Robotic systems for manipulation tasks are increasingly expected to be easy to configure for new tasks or unpredictable environments, while keeping a transparent policy that is readable and verifiable by humans. We propose the method BEhavior TRee eXPansion with Large Language Models (BETR-XP-LLM) to dynamically and automatically expand and configure Behavior Trees as policies for robot control. The method utilizes an LLM to resolve errors outside the task planner's capabilities, both during planning and execution. We show that the method is able to solve a variety of tasks and failures and permanently update the policy to handle similar problems in the future.
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