Title: CoPAL: Corrective Planning of Robot Actions with Large Language Models Show Chinese title

Title: CoPAL：使用大型语言模型的机器人行动纠正性规划

Abstract: In the pursuit of fully autonomous robotic systems capable of taking over tasks traditionally performed by humans, the complexity of open-world environments poses a considerable challenge. Addressing this imperative, this study contributes to the field of Large Language Models (LLMs) applied to task and motion planning for robots. We propose a system architecture that orchestrates a seamless interplay between multiple cognitive levels, encompassing reasoning, planning, and motion generation. At its core lies a novel replanning strategy that handles physically grounded, logical, and semantic errors in the generated plans. We demonstrate the efficacy of the proposed feedback architecture, particularly its impact on executability, correctness, and time complexity via empirical evaluation in the context of a simulation and two intricate real-world scenarios: blocks world, barman and pizza preparation. Show Chinese abstract

Abstract: 在追求能够接管人类传统任务的完全自主机器人系统的过程中，开放世界环境的复杂性构成了相当大的挑战。 为了解决这一迫切需求，本研究致力于将大规模语言模型（LLMs）应用于机器人任务和运动规划领域。 我们提出了一种系统架构，该架构协调多个认知层次之间的无缝互动，包括推理、规划和运动生成。 其核心是一种新颖的重规划策略，能够处理生成计划中的物理接地、逻辑和语义错误。 我们通过仿真以及两个复杂的现实场景（积木世界、调酒师和披萨制作）的实证评估，展示了所提出的反馈架构的有效性，特别是在可执行性、正确性和时间复杂度方面的影响力。