Summary of Pre-trained Text-to-image Diffusion Models Are Versatile Representation Learners For Control, by Gunshi Gupta et al.
Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control
by Gunshi Gupta, Karmesh Yadav, Yarin Gal, Dhruv Batra, Zsolt Kira, Cong Lu, Tim G. J. Rudner
First submitted to arxiv on: 9 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Robotics (cs.RO); Machine Learning (stat.ML)
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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 presents a novel approach to pre-training vision-language models for embodied AI agents. Current contrastively trained representations, such as CLIP, struggle to provide fine-grained scene understanding necessary for control tasks. To address this limitation, the authors propose using pre-trained text-to-image diffusion models, which contain highly fine-grained visuo-spatial information. This approach enables learning of downstream control policies that generalize well to complex environments and outperforms state-of-the-art representation learning approaches on various simulated control settings. Notably, the proposed Stable Control Representations achieve state-of-the-art performance on the challenging OVMM open-vocabulary navigation benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Embodied AI agents need to understand the world in detail from visual and language inputs. Currently, there are no good ways for them to learn this. The authors suggest a new approach using pre-trained models that can generate images based on text prompts. These models contain detailed information about what things look like. By using these models, they can teach AI agents to control objects and navigate through spaces. This is important because it allows the AI agents to perform complex tasks in real-world environments. |
Keywords
» Artificial intelligence » Representation learning » Scene understanding
