Summary of Embodied Agent Interface: Benchmarking Llms For Embodied Decision Making, by Manling Li et al.
Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making
by Manling Li, Shiyu Zhao, Qineng Wang, Kangrui Wang, Yu Zhou, Sanjana Srivastava, Cem Gokmen, Tony Lee, Li Erran Li, Ruohan Zhang, Weiyu Liu, Percy Liang, Li Fei-Fei, Jiayuan Mao, Jiajun Wu
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); 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 This paper aims to evaluate Large Language Models (LLMs) for embodied decision-making tasks. Existing work has applied LLMs in various domains and purposes, but lacks a systematic understanding of their performance due to differences in inputs and outputs. To address this limitation, the authors propose a generalized interface (Embodied Agent Interface) that formalizes different types of tasks and input-output specifications of LLM-based modules. The benchmark unifies embodied decision-making tasks involving state and temporally extended goals, four LLM-based modules for decision making, and fine-grained metrics to assess various errors. This comprehensive evaluation pinpoints the strengths and weaknesses of LLM-powered AI systems, providing insights for effective and selective use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Language Models can help make decisions in situations where a physical body is involved. So far, these models have been used in different ways to solve problems, but we don’t really understand how they’re doing overall. To fix this, the authors created a new way to test these models called the Embodied Agent Interface. This interface helps us compare different types of tasks and see what kind of mistakes the models make. By looking at these mistakes, we can figure out where the models are strong and where they need improvement. |
