iVideoGPT: Interactive VideoGPTs are Scalable World Models

Jialong Wu, Shaofeng Yin, fengningya, Xu He, Dong Li, Jianye Hao, Mingsheng Long

iVideoGPT: Interactive VideoGPTs are Scalable World Models: 16 upvotes on Hugging Face Daily Papers, #9 of 13 papers on 2024-05-27. Day-by-day upvote history.

World models empower model-based agents to interactively explore, reason, and plan within imagined environments for real-world decision-making. However, the high demand for interactivity poses challenges in harnessing recent advancements in video generative models for developing world models at scale. This work introduces Interactive VideoGPT (iVideoGPT), a scalable autoregressive transformer framework that integrates multimodal signals--visual observations, actions, and rewards--into a sequence of tokens, facilitating an interactive experience of agents via next-token prediction. iVideoGPT features a novel compressive tokenization technique that efficiently discretizes high-dimensional visual observations. Leveraging its scalable architecture, we are able to pre-train iVideoGPT on millions of human and robotic manipulation trajectories, establishing a versatile foundation that is adaptable to serve as interactive world models for a wide range of downstream tasks. These include action-conditioned video prediction, visual planning, and model-based reinforcement learning, where iVideoGPT achieves competitive performance compared with state-of-the-art methods. Our work advances the development of interactive general world models, bridging the gap between generative video models and practical model-based reinforcement learning applications.

Paper page on Hugging Face · arXiv

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