This paper addresses task planning problems for language-instructed robot teams. Tasks are expressed in natural language (NL), requiring the robots to apply their capabilities 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 is capable of achieving user-defined mission success rates. This is accomplished by leveraging conformal prediction (CP), a distribution-free uncertainty quantification tool in black-box models. CP allows the proposed multi-robot planner to reason about its inherent uncertainty in a distributed fashion, enabling robots to make individual decisions when they are sufficiently certain 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 overall 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. The advantage of our algorithm over baselines becomes more pronounced with increasing robot team size.

翻译：本文研究面向语言指令机器人团队的任务规划问题。任务以自然语言表述，要求机器人在不同位置和语义对象上应用其能力。近期若干研究通过利用预训练大语言模型设计有效的多机器人规划方案来解决类似问题，但这些方法缺乏性能保证。为应对这一挑战，我们提出一种新型分布式LLM规划器——S-ATLAS（语言指令智能体团队安全规划系统），该系统能够实现用户定义的任务成功率。这一目标通过保形预测实现，该方法是黑盒模型中无需分布假设的不确定性量化工具。保形预测使所提出的多机器人规划器能够以分布式方式推理其内在不确定性，使机器人在足够确定时自主决策，否则寻求协助。我们从理论和实验两方面证明，在假设规划执行成功的前提下，所提出的规划器能够实现用户指定的任务成功率，同时最小化总体协助请求次数。通过对比实验表明，本方法计算效率显著更高且协助率更低。随着机器人团队规模增大，本算法相较于基线方法的优势更加显著。