通过积极和被动修改大型语言模型来增强长期任务规划
Kazuki Hori1, Kanata Suzuki2,3, Tetsuya Ogata2,4,5
1Faculty of Science and Engineering, Waseda University, Tokyo, Japan. horikazuki28@akane.waseda.jp.
Scientific reports
|February 27, 2025
概括
本研究引入了一种新方法,用于使用大型语言模型 (LLM) 创建详细的机器人任务计划. 该方法通过使LLM能够提出澄清问题,改善复杂任务执行来增强规划.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 目前的大型语言模型 (LLM) 产生有限的机器人任务计划 (<=10个命令).
- 远程机器人任务规划仍然是一个重大挑战.
- 现有的方法在复杂的计划中与模糊性和信息丰富性作斗争.
研究的目的:
- 开发一种方法,使用LLMs生成复杂的,长时间的线下机器人任务计划.
- 通过对话进行积极的信息收集来加强LLM驱动的任务规划.
- 改进机器人运动计划的细节和信息内容.
主要方法:
- 提出了一种新的方法,LLM通过提问来积极收集缺少的信息.
- 实施了一个问答流程,允许LLM判断和解决模两可.
- 通过积极和被动的修改,利用对话来完善任务计划.
- 定义任务计划项目作为机器人执行关键信息.
主要成果:
- 提出的方法成功地增加了任务计划中的信息量.
- 基于对话的改进导致了更详细和更全面的计划.
- 实验证明了使用任务场景的有效性.
结论:
- 以LLM为驱动的对话对于生成复杂和信息丰富的机器人任务计划是有效的.
- 通过LLM积极收集信息,显著提高了长期任务的规划能力.
- 该方法提供了一种有希望的方法来克服目前基于LLM的机器人规划的局限性.
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