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[2602.13347] Visual Foresight for Robotic Stow: A Diffusion-Based World Model from Sparse Snapshots
Summary
The paper presents FOREST, a diffusion-based world model for robotic stow operations, enhancing the prediction of post-stow configurations in automated warehouses.
Why It Matters
As automated warehouses grow, improving the efficiency of stow operations is crucial. This research offers a novel approach to anticipate storage layouts, which can optimize warehouse management and reduce operational costs, making it relevant for industries relying on automation.
Key Takeaways
- FOREST improves the geometric accuracy of predicted post-stow layouts.
- The model utilizes item-aligned instance masks for better representation.
- Evaluation shows modest performance loss in downstream tasks when using FOREST predictions.
- This approach can enhance foresight signals for warehouse planning.
- The research contributes to advancements in robotic automation and AI applications.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.13347 (cs) [Submitted on 12 Feb 2026] Title:Visual Foresight for Robotic Stow: A Diffusion-Based World Model from Sparse Snapshots Authors:Lijun Zhang, Nikhil Chacko, Petter Nilsson, Ruinian Xu, Shantanu Thakar, Bai Lou, Harpreet Sawhney, Zhebin Zhang, Mudit Agrawal, Bhavana Chandrashekhar, Aaron Parness View a PDF of the paper titled Visual Foresight for Robotic Stow: A Diffusion-Based World Model from Sparse Snapshots, by Lijun Zhang and 10 other authors View PDF HTML (experimental) Abstract:Automated warehouses execute millions of stow operations, where robots place objects into storage bins. For these systems it is valuable to anticipate how a bin will look from the current observations and the planned stow behavior before real execution. We propose FOREST, a stow-intent-conditioned world model that represents bin states as item-aligned instance masks and uses a latent diffusion transformer to predict the post-stow configuration from the observed context. Our evaluation shows that FOREST substantially improves the geometric agreement between predicted and true post-stow layouts compared with heuristic baselines. We further evaluate the predicted post-stow layouts in two downstream tasks, in which replacing the real post-stow masks with FOREST predictions causes only modest performance loss in load-quality assessment and multi-stow reasoning, indicating that our model can provide useful foresight sign...
