DKPROMPT：面向开放世界规划的领域知识提示视觉语言模型
DKPROMPT: Domain Knowledge Prompting Vision-Language Models for Open-World Planning
AI总结：
本文提出DKPROMPT框架，利用PDDL领域知识自动化提示VLM，解决开放世界中机器人任务规划问题，在任务完成率上优于经典规划、纯VLM及竞争基线。
摘要：
视觉语言模型（VLMs）已被应用于机器人任务规划问题，其中机器人接收自然语言任务并基于视觉输入生成规划。尽管当前的VLMs展现了强大的视觉语言理解能力，但其在规划任务中的表现仍远未令人满意。同时，尽管基于PDDL等经典任务规划器在长程任务规划方面表现出色，但它们在充满不可预见情况的开放世界中表现不佳。本文提出了一种名为DKPROMPT的新型任务规划与执行框架，该框架利用PDDL中的领域知识自动化VLM提示，以在开放世界中进行经典规划。定量实验结果表明，DKPROMPT在任务完成率上优于经典规划、纯VLM方法及其他竞争性基线。
英文摘要：
Vision-language models (VLMs) have been applied to robot task planning problems, where the robot receives a task in natural language and generates plans based on visual inputs. While current VLMs have demonstrated strong vision-language understanding capabilities, their performance is still far from being satisfactory in planning tasks. At the same time, although classical task planners, such as PDDL-based, are strong in planning for long-horizon tasks, they do not work well in open worlds where unforeseen situations are common. In this paper, we propose a novel task planning and execution framework, called DKPROMPT, which automates VLM prompting using domain knowledge in PDDL for classical planning in open worlds. Results from quantitative experiments show that DKPROMPT outperforms classical planning, pure VLM-based and a few other competitive baselines in task completion rate.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
