Summary of Ivideogpt: Interactive Videogpts Are Scalable World Models, by Jialong Wu et al.
iVideoGPT: Interactive VideoGPTs are Scalable World Models
by Jialong Wu, Shaofeng Yin, Ningya Feng, Xu He, Dong Li, Jianye Hao, Mingsheng Long
First submitted to arxiv on: 24 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The paper introduces Interactive VideoGPT (iVideoGPT), a scalable autoregressive transformer framework that integrates multimodal signals to facilitate an interactive experience of agents via next-token prediction. iVideoGPT features a novel compressive tokenization technique that efficiently discretizes high-dimensional visual observations, allowing for pre-training on millions of human and robotic manipulation trajectories. The framework is adaptable to serve as interactive world models for various downstream tasks, including action-conditioned video prediction, visual planning, and model-based reinforcement learning. iVideoGPT achieves competitive performance compared with state-of-the-art methods in these tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to help machines learn about the world by interacting with it. They developed a model called Interactive VideoGPT that can predict what will happen next based on what’s happening now. This helps agents, like robots, make better decisions and learn from their experiences. The model is really good at understanding different types of signals, like pictures, actions, and rewards. It’s also very fast and efficient, which makes it useful for lots of different tasks. |
Keywords
» Artificial intelligence » Autoregressive » Reinforcement learning » Token » Tokenization » Transformer
