Summary of Dkprompt: Domain Knowledge Prompting Vision-language Models For Open-world Planning, by Xiaohan Zhang et al.
DKPROMPT: Domain Knowledge Prompting Vision-Language Models for Open-World Planning
by Xiaohan Zhang, Zainab Altaweel, Yohei Hayamizu, Yan Ding, Saeid Amiri, Hao Yang, Andy Kaminski, Chad Esselink, Shiqi Zhang
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, DKPROMPT, revolutionizes robot task planning by integrating vision-language models (VLMs) with domain knowledge in PDDL for classical planning. This medium-difficulty summary highlights the significance of DKPROMPT in addressing open-world challenges and outperforming current baselines. By leveraging VLMs’ natural language understanding capabilities and PDDL’s long-horizon planning strengths, DKPROMPT automates task prompting using domain knowledge, showcasing improved task completion rates compared to classical planning, pure VLM-based approaches, and other competitive baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DKPROMPT is a new way for robots to understand tasks in natural language and make plans. Right now, machines can only do this job well if they have plenty of time to think about it. But what happens when things don’t go as planned? That’s where DKPROMPT comes in. It combines two strong tools: vision-language models that are great at understanding words and PDDL, a way to plan for big tasks. By using domain knowledge, DKPROMPT makes robots better planners, even in unexpected situations. |
Keywords
» Artificial intelligence » Language understanding » Prompting
