Efficient and safe retrieval of stacked objects in warehouse environments is a significant challenge due to complex spatial dependencies and structural inter-dependencies. Traditional vision-based methods excel at object localization but often lack the physical reasoning required to predict the consequences of extraction, leading to unintended collisions and collapses. This paper proposes a collapse and collision aware grasp planner that integrates dynamic physics simulations for robotic decision-making. Using a single image and depth map, an approximate 3D representation of the scene is reconstructed in a simulation environment, enabling the robot to evaluate different retrieval strategies before execution. Two approaches 1) heuristic-based and 2) physics-based are proposed for both single-box extraction and shelf clearance tasks. Extensive real-world experiments on structured and unstructured box stacks, along with validation using datasets from existing databases, show that our physics-aware method significantly improves efficiency and success rates compared to baseline heuristics.

翻译：在仓库环境中高效安全地提取堆叠物体是一项重大挑战，这源于复杂的空间依赖性与结构互锁性。传统基于视觉的方法在物体定位方面表现出色，但通常缺乏预测提取后果所需的物理推理能力，从而导致意外的碰撞与坍塌。本文提出一种集成动态物理仿真的坍塌与碰撞感知抓取规划器，用于机器人决策。该方法利用单幅图像与深度图，在仿真环境中重建场景的近似三维表征，使机器人能够在执行前评估不同的抓取策略。针对单箱提取与货架清空任务，我们提出了两种方法：1) 基于启发式规则；2) 基于物理仿真。通过在结构化与非结构化箱体堆垛上进行大量真实环境实验，并结合现有数据库数据集的验证，结果表明：相较于基准启发式方法，我们的物理感知方法能显著提升作业效率与成功率。