Text2Reaction : Enabling Reactive Task Planning Using Large Language Models
文献类型：期刊论文
| 作者 | Yang, Zejun2,3; Ning, Li2,3; Wang, Haitao1,3; Jiang, Tianyu1,3; Zhang, Shaolin1,3; Cui, Shaowei1,3; Jiang, Hao2,3; Li, Chunpeng2,3; Wang, Shuo1,3; Wang, Zhaoqi2,3 |
| 刊名 | IEEE ROBOTICS AND AUTOMATION LETTERS
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 9期号:5页码:4003-4010 |
| ISSN号 | 2377-3766 |
| 关键词 | Planning under uncertainty AI-based methods learning from demonstration |
| DOI | 10.1109/LRA.2024.3371223 |
| 英文摘要 | To complete tasks in dynamic environments, robots need to timely update their plans to react to environment changes. Traditional stripe-like or learning-based planners struggle to achieve this due to their high reliance on meticulously predefined planning rules or labeled data. Fortunately, recent works find that Large Language Models (LLMs) can be effectively prompted to solve planning problems. Thus, we investigate the strategies for LLMs to master reactive planning problems without complex definitions and extra training. We propose Text2Reaction, an LLM-based framework enabling robots to continuously reason and update plans according to the latest environment changes. Inspired from human's step-by-step re-planning process, we present the Re-planning Prompt, which informs LLMs the basic principles of re-planning and fosters the gradual development of a current plan to a new one in a three-hop reasoning manner-cause analysis, consequence inference, and plan adjustment. In addition, Text2Reaction is designed to first generate an initial plan based on the task description before execution, allowing for subsequent iterative updates of this plan. We demonstrate the superior performance of Text2Reaction over prior works in reacting to various environment changes and completing varied tasks. In addition, we validate the reliability of our re-planning prompt through ablation experiments and its capability when deployed in real-world robots, enabling continuous reasoning in the face of diverse changes until the user instructions are successfully completed. |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Robotics |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001189843900003 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/38758]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Cui, Shaowei; Jiang, Hao |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Zejun,Ning, Li,Wang, Haitao,et al. Text2Reaction : Enabling Reactive Task Planning Using Large Language Models[J]. IEEE ROBOTICS AND AUTOMATION LETTERS,2024,9(5):4003-4010. |
| APA | Yang, Zejun.,Ning, Li.,Wang, Haitao.,Jiang, Tianyu.,Zhang, Shaolin.,...&Wang, Zhaoqi.(2024).Text2Reaction : Enabling Reactive Task Planning Using Large Language Models.IEEE ROBOTICS AND AUTOMATION LETTERS,9(5),4003-4010. |
| MLA | Yang, Zejun,et al."Text2Reaction : Enabling Reactive Task Planning Using Large Language Models".IEEE ROBOTICS AND AUTOMATION LETTERS 9.5(2024):4003-4010. |
入库方式： OAI收割
来源：计算技术研究所
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