Large-Scale Actionless Video Pre-Training via Discrete Diffusion for Efficient Policy Learning

• URL: http://arxiv.org/abs/2402.14407v1
• Date: Thu, 22 Feb 2024 09:48:47 GMT
• Title: Large-Scale Actionless Video Pre-Training via Discrete Diffusion for Efficient Policy Learning
• Authors: Haoran He, Chenjia Bai, Ling Pan, Weinan Zhang, Bin Zhao, Xuelong Li
• Abstract summary: We introduce a novel framework that combines generative pre-training on human videos and policy fine-tuning on action-labeled robot videos. Our method generates high-fidelity future videos for planning and enhances the fine-tuned policies compared to previous state-of-the-art approaches.
• Score: 73.69573252516761
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning a generalist embodied agent capable of completing multiple tasks poses challenges, primarily stemming from the scarcity of action-labeled robotic datasets. In contrast, a vast amount of human videos exist, capturing intricate tasks and interactions with the physical world. Promising prospects arise for utilizing actionless human videos for pre-training and transferring the knowledge to facilitate robot policy learning through limited robot demonstrations. In this paper, we introduce a novel framework that leverages a unified discrete diffusion to combine generative pre-training on human videos and policy fine-tuning on a small number of action-labeled robot videos. We start by compressing both human and robot videos into unified video tokens. In the pre-training stage, we employ a discrete diffusion model with a mask-and-replace diffusion strategy to predict future video tokens in the latent space. In the fine-tuning stage, we harness the imagined future videos to guide low-level action learning trained on a limited set of robot data. Experiments demonstrate that our method generates high-fidelity future videos for planning and enhances the fine-tuned policies compared to previous state-of-the-art approaches with superior generalization ability. Our project website is available at https://video-diff.github.io/.

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