Probabilistically Correct Language-Based Multi-Robot Planning Using Conformal Prediction

IF 4.6 2区 计算机科学 Q2 ROBOTICS
Jun Wang;Guocheng He;Yiannis Kantaros
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Abstract

This paper addresses task planning problems for language-instructed robot teams. Tasks are expressed in natural language (NL), requiring the robots to apply their skills at various locations and semantic objects. Several recent works have addressed similar planning problems by leveraging pre-trained Large Language Models (LLMs) to design effective multi-robot plans. However, these approaches lack performance guarantees. To address this challenge, we introduce a new distributed LLM-based planner, called S-ATLAS for Safe plAnning for Teams of Language-instructed AgentS, that can achieve user-defined mission success rates. This is accomplished by leveraging conformal prediction (CP), a distribution-free uncertainty quantification tool. CP allows the proposed multi-robot planner to reason about its inherent uncertainty, due to imperfections of LLMs, in a distributed fashion, enabling robots to make local decisions when they are sufficiently confident and seek help otherwise. We show, both theoretically and empirically, that the proposed planner can achieve user-specified task success rates, assuming successful plan execution, while minimizing the average number of help requests. We provide comparative experiments against related works showing that our method is significantly more computational efficient and achieves lower help rates.
基于保形预测的概率正确语言多机器人规划
本文讨论了语言指导机器人团队的任务规划问题。任务用自然语言(NL)表达,要求机器人在不同的位置和语义对象上应用他们的技能。最近的一些工作通过利用预训练的大型语言模型(llm)来设计有效的多机器人计划,解决了类似的规划问题。然而,这些方法缺乏性能保证。为了应对这一挑战,我们引入了一种新的基于llm的分布式规划器,称为S-ATLAS,用于语言指导代理团队的安全规划,可以实现用户定义的任务成功率。这是通过利用保形预测(CP)来实现的,保形预测是一种无分布的不确定性量化工具。CP允许所提出的多机器人规划器以分布式方式推理其固有的不确定性,由于llm的不完善,使机器人能够在足够自信时做出局部决策,否则就会寻求帮助。我们在理论上和经验上都表明,假设计划执行成功,建议的计划器可以实现用户指定的任务成功率,同时最小化帮助请求的平均数量。我们提供了与相关工作的对比实验,表明我们的方法显着提高了计算效率并实现了更低的帮助率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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