MinD: Unified Visual Imagination and Control via Hierarchical World Models

• URL: http://arxiv.org/abs/2506.18897v1
• Date: Mon, 23 Jun 2025 17:59:06 GMT
• Title: MinD: Unified Visual Imagination and Control via Hierarchical World Models
• Authors: Xiaowei Chi, Kuangzhi Ge, Jiaming Liu, Siyuan Zhou, Peidong Jia, Zichen He, Yuzhen Liu, Tingguang Li, Lei Han, Sirui Han, Shanghang Zhang, Yike Guo,
• Abstract summary: Video generation models (VGMs) offer a promising pathway for unified world modeling in robotics.<n>Manipulate in Dream (MinD) is a hierarchical diffusion-based world model framework that employs a dual-system design for vision-language manipulation.<n>MinD executes VGM at low frequencies to extract video prediction features, while leveraging a high-frequency diffusion policy for real-time interaction.
• Score: 32.08769443927576
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video generation models (VGMs) offer a promising pathway for unified world modeling in robotics by integrating simulation, prediction, and manipulation. However, their practical application remains limited due to (1) slowgeneration speed, which limits real-time interaction, and (2) poor consistency between imagined videos and executable actions. To address these challenges, we propose Manipulate in Dream (MinD), a hierarchical diffusion-based world model framework that employs a dual-system design for vision-language manipulation. MinD executes VGM at low frequencies to extract video prediction features, while leveraging a high-frequency diffusion policy for real-time interaction. This architecture enables low-latency, closed-loop control in manipulation with coherent visual guidance. To better coordinate the two systems, we introduce a video-action diffusion matching module (DiffMatcher), with a novel co-training strategy that uses separate schedulers for each diffusion model. Specifically, we introduce a diffusion-forcing mechanism to DiffMatcher that aligns their intermediate representations during training, helping the fast action model better understand video-based predictions. Beyond manipulation, MinD also functions as a world simulator, reliably predicting task success or failure in latent space before execution. Trustworthy analysis further shows that VGMs can preemptively evaluate task feasibility and mitigate risks. Extensive experiments across multiple benchmarks demonstrate that MinD achieves state-of-the-art manipulation (63%+) in RL-Bench, advancing the frontier of unified world modeling in robotics.

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