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 (e.g., mobility, manipulation, and sensing) 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 mission performance and safety guarantees. To address this challenge, we introduce a new decentralized LLM-based planner that is capable of achieving high 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 decentralized 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 while minimizing the overall number of help requests. We demonstrate the performance of our approach on multi-robot home service applications. We also show through comparative experiments, that our method outperforms recent centralized and decentralized multi-robot LLM-based planners in terms of in terms of its ability to design correct plans. The advantage of our algorithm over baselines becomes more pronounced with increasing mission complexity and robot team size.

翻译：本文研究了语言指令机器人团队的任务规划问题。任务以自然语言表达，要求机器人在地点与语义对象上发挥其能力（如移动、操作及感知）。近期多项工作通过利用预训练大型语言模型解决类似规划问题以实现多机器人高效规划，但这些方法缺乏任务性能与安全保障。针对此挑战，我们提出一种新型去中心化的大语言模型规划器，能够在保证高任务成功率的同时，借助共形预测——一种黑箱模型中无分布假设的不确定性量化工具——实现这一目标。共形预测使所提多机器人规划器能去中心化地推理其内在不确定性，使机器人在充分确定时自主决策，否则请求协助。我们从理论与实验两方面证明，所提规划器能在最小化总求助次数的前提下实现用户指定的任务成功率。我们在多机器人家庭服务应用中展示了该方法性能。对比实验表明，本方法在规划正确性方面优于近期集中式与去中心化多机器人LLM规划器，且随着任务复杂度与机器人团队规模的增加，本方法相较基线的优势愈发显著。