Summary of Selu: Self-learning Embodied Mllms in Unknown Environments, by Boyu Li et al.
SELU: Self-Learning Embodied MLLMs in Unknown Environments
by Boyu Li, Haobin Jiang, Ziluo Ding, Xinrun Xu, Haoran Li, Dongbin Zhao, Zongqing Lu
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 proposes a novel approach to enhancing the environmental comprehension of multimodal large language models (MLLMs) in unknown environments. The method, called SELU, combines an actor-critic paradigm inspired by reinforcement learning with self-asking and hindsight relabeling. This allows the critic to extract knowledge from interaction trajectories collected by the actor, improving its understanding of the environment. Simultaneously, the actor is improved by the self-feedback provided by the critic, enhancing its decision-making capabilities. The proposed method is evaluated in two environments, AI2-THOR and VirtualHome, achieving significant improvements in both environmental comprehension (approximately 28% and 30%) and decision-making (about 20% and 24%). This work has implications for the development of autonomously improving MLLMs that can operate effectively in unknown environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help large language models understand their environment better. These models are really good at learning and making decisions, but they need feedback from humans or the environment itself to improve. The researchers propose a method called SELU that helps these models learn from their own experiences and make better decisions. They tested this method in two simulated environments and found that it improved the model’s understanding of its surroundings by about 28% to 30%, and also made its decisions more accurate by around 20% to 24%. This is important because it could help create language models that can work independently, making them useful for many applications. |
Keywords
* Artificial intelligence * Reinforcement learning
