Automated task planning algorithms have been developed to help robots complete complex tasks that require multiple actions. Most of those algorithms have been developed for "closed worlds" assuming complete world knowledge is provided. However, the real world is generally open, and the robots frequently encounter unforeseen situations that can potentially break the planner's completeness. This paper introduces a novel algorithm (COWP) for open-world task planning and situation handling that dynamically augments the robot's action knowledge with task-oriented common sense. In particular, common sense is extracted from Large Language Models based on the current task at hand and robot skills. For systematic evaluations, we collected a dataset that includes 561 execution-time situations in a dining domain, where each situation corresponds to a state instance of a robot being potentially unable to complete a task using a solution that normally works. Experimental results show that our approach significantly outperforms competitive baselines from the literature in the success rate of service tasks. Additionally, we have demonstrated COWP using a mobile manipulator. The project website is available at: https://cowplanning.github.io/, where a more detailed version can also be found. This version has been accepted for publication in Autonomous Robots.

翻译：自动化任务规划算法已被开发用于帮助机器人完成需要多个动作的复杂任务。大多数此类算法是为"封闭世界"设计的，其假设提供了完整的世界知识。然而，现实世界通常是开放的，机器人经常会遇到不可预见的情境，这些情境可能破坏规划器的完备性。本文提出了一种用于开放世界任务规划和情境处理的新算法（COWP），该算法通过面向任务的常识动态增强机器人的动作知识。具体而言，常识是基于当前任务和机器人技能从大型语言模型中提取的。为了进行系统评估，我们收集了一个包含餐饮领域561个执行时情境的数据集，其中每个情境对应机器人可能无法使用通常有效的解决方案完成任务的一个状态实例。实验结果表明，我们的方法在服务任务的成功率上显著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。项目网站位于：https://cowplanning.github.io/，更详细版本也可在该网站获取。本版本已被《Autonomous Robots》期刊录用发表。