Summary of Multi-agent Planning Using Visual Language Models, by Michele Brienza et al.
Multi-Agent Planning Using Visual Language Models
by Michele Brienza, Francesco Argenziano, Vincenzo Suriani, Domenico D. Bloisi, Daniele Nardi
First submitted to arxiv on: 10 Aug 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 paper proposes a multi-agent architecture for embodied task planning that can operate without specific data structures as input. This approach leverages commonsense knowledge and uses a single image of the environment, making it suitable for free-form domains. The proposed method is compared to existing methods using the widely recognized ALFRED dataset, with a novel evaluation procedure called PG2S being introduced to better assess the quality of generated plans. Large Language Models (LLMs) and Visual Language Models (VLMs) are gaining popularity due to their improving performance and applications across various domains and tasks. However, these models can produce erroneous results when a deep understanding of the problem domain is required. The proposed approach addresses this issue by providing a more effective way for embodied task planning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to plan and make decisions based on what we see around us. It’s called “embodied task planning” and it works by using a single image of the environment, without needing specific information about where things are. This makes it useful for situations where there’s no clear structure or rules. The approach is tested using a dataset called ALFRED and a new way to evaluate how well plans work. Large Language Models and Visual Language Models are getting better at doing things like this, but they can also make mistakes if the situation is complex. This paper provides a more effective way for embodied task planning. |
