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. Supplementary materials are available at:
this https URL

介绍了一种新的算法 (COWP)，用于基于任务和机器人技能从语言模型中提取常识，实现在开放世界中完成任务规划和情境处理，实验结果表明其在服务任务成功率方面显著优于现有文献中的竞争基线算法。