Summary of Grounding Multimodal Large Language Models in Actions, by Andrew Szot et al.
Grounding Multimodal Large Language Models in Actions
by Andrew Szot, Bogdan Mazoure, Harsh Agrawal, Devon Hjelm, Zsolt Kira, Alexander Toshev
First submitted to arxiv on: 12 Jun 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 paper investigates how to effectively ground Multimodal Large Language Models (MLLMs) in various embodiments and their associated action spaces. The goal is to leverage the MLLM’s multimodal world knowledge for tasks such as Embodied AI. To achieve this, the authors develop a unified architecture and action space adapters, demonstrating that learned tokenization outperforms other methods for continuous actions and semantic alignment yields the strongest performance for discrete actions. Through experiments on seven action space adapters across five environments, encompassing over 114 embodied tasks, the study provides insights into the best approaches for grounding MLLMs in different embodiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching a special kind of computer program called Multimodal Large Language Models (MLLMs) to work with different types of robots and actions. The goal is to make these programs smarter by using what they’ve learned from the world to solve problems. To do this, the authors create new ways to connect the MLLM to the robot’s movements or actions. They find that two different methods work best: one for actions that can be anything (continuous), and another for actions that are specific choices (discrete). By testing these methods on many different robots and actions, they show how to make MLLMs more useful in real-world situations. |
Keywords
» Artificial intelligence » Alignment » Grounding » Tokenization
