Summary of Mfe-etp: a Comprehensive Evaluation Benchmark For Multi-modal Foundation Models on Embodied Task Planning, by Min Zhang et al.
MFE-ETP: A Comprehensive Evaluation Benchmark for Multi-modal Foundation Models on Embodied Task Planning
by Min Zhang, Xian Fu, Jianye Hao, Peilong Han, Hao Zhang, Lei Shi, Hongyao Tang, Yan Zheng
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 investigates the performance of Multi-modal Foundation Models (MFMs) on embodied task planning, aiming to understand their capabilities and limitations in this domain. The authors develop a systematic evaluation framework that assesses MFMs’ object understanding, spatio-temporal perception, task understanding, and embodied reasoning capabilities. They propose a new benchmark, MFE-ETP, featuring complex task scenarios, diverse task types, and varying difficulty levels. An automatic evaluation platform is also introduced, allowing for the testing of multiple MFMs on this benchmark. The authors evaluate several state-of-the-art MFMs using the proposed framework and find that they lag behind human-level performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well a type of AI model called Multi-modal Foundation Models (MFMs) do when planning tasks in the real world. MFM s are special because they can understand different types of data, like images or words. The researchers created a way to test these models’ skills and came up with a new set of challenges that mimic real-life situations. They tested several top-performing MFMs on this benchmark and found that they still have a lot to learn before reaching human-level abilities. |
Keywords
» Artificial intelligence » Multi modal
