Summary of From Multimodal Llms to Generalist Embodied Agents: Methods and Lessons, by Andrew Szot et al.
From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons
by Andrew Szot, Bogdan Mazoure, Omar Attia, Aleksei Timofeev, Harsh Agrawal, Devon Hjelm, Zhe Gan, Zsolt Kira, Alexander Toshev
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This research paper investigates the capabilities of Multimodal Large Language Models (MLLMs) in tackling various domains beyond traditional language and vision tasks. Specifically, the study focuses on areas such as Embodied AI, Games, UI Control, and Planning. The researchers introduce a process to adapt an MLLM to a Generalist Embodied Agent (GEA), a single unified model capable of grounding itself across these varied domains through a multi-embodiment action tokenizer. GEA is trained using supervised learning on a large dataset of embodied experiences and online RL in interactive simulators. The findings reveal the importance of training with cross-domain data and online RL for building generalist agents, demonstrating strong generalization performance to unseen tasks across diverse benchmarks compared to other generalist models and benchmark-specific approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can be used in many different areas like robots, games, and planning. They create a special type of model that can understand and work with these different areas by learning from a large dataset and practicing in simulated environments. The results show that this approach is better than just using the same technique for each area separately. |
Keywords
» Artificial intelligence » Generalization » Grounding » Supervised » Tokenizer
