Visual Foresight for Robotic Stow: A Diffusion-Based World Model from Sparse Snapshots
This addresses a domain-specific problem for warehouse robotics, offering incremental improvements in stow planning.
The paper tackles the problem of predicting post-stow configurations in automated warehouses to anticipate bin layouts before execution, and shows that FOREST improves geometric agreement with true layouts and causes only modest performance loss in downstream tasks.
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 signals for warehouse planning.