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[2506.01392] Sparse Imagination for Efficient Visual World Model Planning
Summary
The paper presents a novel approach called Sparse Imagination for enhancing visual world model planning in robotics, improving computational efficiency while maintaining task performance.
Why It Matters
As robotics applications become more complex, efficient planning methods are crucial for real-time decision-making. This research addresses computational limitations, potentially transforming how robots operate in dynamic environments by allowing for faster and more efficient planning.
Key Takeaways
- Introduces Sparse Imagination to enhance visual world model planning.
- Reduces computational burden by processing fewer tokens during predictions.
- Maintains high control fidelity while improving inference efficiency.
- Applicable to both simple and complex real-world tasks.
- Enables real-time deployment of world models in robotics.
Computer Science > Robotics arXiv:2506.01392 (cs) [Submitted on 2 Jun 2025 (v1), last revised 26 Feb 2026 (this version, v2)] Title:Sparse Imagination for Efficient Visual World Model Planning Authors:Junha Chun, Youngjoon Jeong, Taesup Kim View a PDF of the paper titled Sparse Imagination for Efficient Visual World Model Planning, by Junha Chun and 1 other authors View PDF HTML (experimental) Abstract:World model based planning has significantly improved decision-making in complex environments by enabling agents to simulate future states and make informed choices. This computational burden is particularly restrictive in robotics, where resources are severely constrained. To address this limitation, we propose a Sparse Imagination for Efficient Visual World Model Planning, which enhances computational efficiency by reducing the number of tokens processed during forward prediction. Our method leverages a sparsely trained vision-based world model based on transformers with randomized grouped attention strategy, allowing the model to flexibly adjust the number of tokens processed based on the computational resource. By enabling sparse imagination during latent rollout, our approach significantly accelerates planning while maintaining high control fidelity. Experimental results demonstrate that sparse imagination preserves task performance while dramatically improving inference efficiency. This general technique for visual planning is applicable from simple test-time trajector...
