论文标题
机器人任务计划和处境处理
Robot Task Planning and Situation Handling in Open Worlds
论文作者
论文摘要
已经开发了自动化任务计划算法，以帮助机器人完成需要多个操作的复杂任务。假设提供了完整的世界知识，大多数这些算法都是为“封闭世界”开发的。但是，现实世界通常是开放的，机器人经常遇到可能破坏计划者完整性的不可预见情况。本文介绍了一种新颖的算法（COWP），用于开放世界任务计划和情况处理，该算法通过以任务为导向的常识动态增强机器人的动作知识。特别是，基于当前手工和机器人技能的当前任务从大语言模型中提取常识。对于系统评估，我们收集了一个数据集，该数据集在用餐域中包括561个执行时间情况，在这种情况下，每种情况都对应于机器人可能无法使用通常工作的解决方案完成任务的状态实例。实验结果表明，我们的方法在服务任务的成功率中显着优于文献中的竞争基准。此外，我们还使用移动操纵器展示了COWP。项目网站可在以下网站上找到：https：//cowplanning.github.io/，也可以找到更详细的版本。此版本已被接受以在自主机器人中出版。
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.
