Task planning systems have been developed to help robots use human knowledge (about actions) to complete long-horizon tasks. Most of them have been developed for "closed worlds" while assuming the robot is provided with complete world knowledge. However, the real world is generally open, and the robots frequently encounter unforeseen situations that can potentially break the planner's completeness. Could we leverage the recent advances on pre-trained Large Language Models (LLMs) to enable classical planning systems to deal with novel situations? This paper introduces a novel framework, called COWP, for open-world task planning and situation handling. COWP dynamically augments the robot's action knowledge, including the preconditions and effects of actions, with task-oriented commonsense knowledge. COWP embraces the openness from LLMs, and is grounded to specific domains via action knowledge. For systematic evaluations, we collected a dataset that includes 1,085 execution-time situations. Each situation corresponds to a state instance wherein a robot is potentially unable to complete a task using a solution that normally works. Experimental results show that our approach outperforms competitive baselines from the literature in the success rate of service tasks. Additionally, we have demonstrated COWP using a mobile manipulator. Supplementary materials are available at: https://cowplanning.github.io/

翻译：任务规划系统已被开发用于帮助机器人利用人类知识（关于动作）完成长期任务。然而，大多数系统是为“封闭世界”设计的，假设机器人具备完整的先验世界知识。但现实世界通常是开放的，机器人会频繁遇到可能破坏规划器完备性的意外情境。我们能否利用预训练大语言模型（LLMs）的最新进展，使经典规划系统能够处理新型情境？本文提出了一种称为COWP的新型框架，用于开放世界任务规划与情境处理。COWP通过面向任务的常识知识动态增强机器人的动作知识（包括动作的前提条件和效果），既吸收了LLMs的开放性，又通过动作知识将其锚定到特定领域。为进行系统评估，我们收集了包含1,085个执行时情境的数据集，每个情境对应一个状态实例，其中机器人可能无法使用常规方案完成任务。实验结果表明，我们的方法在服务任务成功率上优于文献中的竞争基线。此外，我们已在移动操作机器人上演示了COWP。补充材料见：https://cowplanning.github.io/