Visual Foresight for Robotic Stow: A Diffusion-Based World Model from Sparse Snapshots

2026-02-12

AI Summary

This work addresses the challenge of anticipating post-placement bin states in automated warehousing to enable efficient robotic planning. The authors propose FOREST, a world model that uniquely integrates diffusion models with instance-level geometric alignment to predict high-fidelity object layouts from current observations and placement intents. By leveraging a latent diffusion Transformer, FOREST represents the bin state as instance masks, capturing fine-grained spatial configurations. Notably, it achieves geometrically consistent predictions using only sparse observational snapshots. Evaluated on load quality assessment and multi-step placement reasoning tasks, FOREST’s predictions closely approximate ground-truth states and significantly outperform heuristic baselines, offering a reliable visual foresight signal for downstream warehouse decision-making.

Technology Category

• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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 signals for warehouse planning.

Problem

• visual foresight
• robotic stow
• world model
• post-stow prediction
• warehouse automation

Innovation

• diffusion-based world model
• visual foresight
• stow-intent-conditioned
• instance mask representation
• latent diffusion transformer