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（关于行动）来完成长期任务。然而，其中大多数系统是为“封闭世界”设计的，假设机器人具备完整的世界知识。但现实世界通常是开放的，机器人经常遭遇不可预见的情境，这些情境可能破坏规划器的完备性。我们能否利用近期预训练大语言模型（LLMs）的进展，使经典规划系统能够应对新情境？本文提出一种名为COWP的新框架，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，动态增强机器人的行动知识（包括行动的前提条件和效果）。COWP融合了LLMs的开放性，并通过行动知识锚定到特定领域。为进行系统性评估，我们收集了一个包含1,085个执行期情境的数据集。每个情境对应一个状态实例，其中机器人使用通常有效的解决方案可能无法完成任务。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，我们已使用移动机械臂演示了COWP。补充材料见：https://cowplanning.github.io/