用扩散模型预测机器人放物后货箱布局，提升仓储自动化规划效率。
Visual Foresight for Robotic Stow: A Diffusion-Based World Model from Sparse Snapshots
- 基于稀疏快照构建物品对齐的实例掩码，用扩散变换器预测放物后状态。
- 预测布局与真实结果几何吻合度显著优于传统启发式方法。
- 适用于仓储规划、多步放物推理等下游任务，可替代真实掩码使用。
自动化仓库执行数百万次货物存放操作，机器人需将物品放入储物箱。提前预判在当前观测和计划行为下的箱子最终布局对系统至关重要。我们提出 FOREST——一种受存放意图约束的世界模型，将箱子状态表示为物品对齐的实例掩码，并利用潜在扩散变换器从观测上下文预测存放后的配置。评估显示，相较于启发式基线，FOREST 在预测布局与真实布局之间的几何一致性上大幅提升。我们在两项下游任务中进一步验证了预测结果：用 FOREST 预测掩码替换真实掩码仅导致负载质量评估和多步存放推理性能小幅下降，表明该模型能为仓储规划提供有效前瞻信号。
原文摘要 · Abstract (English)
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.
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