Efficiently finding an occluded object with lateral access arises in many contexts such as warehouses, retail, healthcare, shipping, and homes. We introduce LAX-RAY (Lateral Access maXimal Reduction of occupancY support Area), a system to automate the mechanical search for occluded objects on shelves. For such lateral access environments, LAX-RAY couples a perception pipeline predicting a target object occupancy support distribution with a mechanical search policy that sequentially selects occluding objects to push to the side to reveal the target as efficiently as possible. Within the context of extruded polygonal objects and a stationary target with a known aspect ratio, we explore three lateral access search policies: Distribution Area Reduction (DAR), Distribution Entropy Reduction (DER), and Distribution Entropy Reduction over Multiple Time Steps (DER-MT) utilizing the support distribution and prior information. We evaluate these policies using the First-Order Shelf Simulator (FOSS) in which we simulate 800 random shelf environments of varying difficulty, and in a physical shelf environment with a Fetch robot and an embedded PrimeSense RGBD Camera. Average simulation results of 87.3% success rate demonstrate better performance of DER-MT with 2 prediction steps. When deployed on the robot, results show a success rate of at least 80% for all policies, suggesting that LAX-RAY can efficiently reveal the target object in reality. Both results show significantly better performance of the three proposed policies compared to a baseline policy with uniform probability distribution assumption in non-trivial cases, showing the importance of distribution prediction. Code, videos, and supplementary material can be found at https://sites.google.com/berkeley.edu/lax-ray.

中文翻译：

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使用横向访问X射线对货架进行机械搜索

在许多情况下，例如仓库，零售，医疗保健，运输和房屋中，有效找到具有横向入口的闭塞物体成为了现实。我们引入了LAX-RAY（占用支持区域的横向访问最大程度减少），该系统可以自动机械搜索货架上被遮挡的物体。对于这样的横向访问环境，LAX-RAY将预测目标对象占用支持分布的感知管道与机械搜索策略结合在一起，该策略依次选择阻塞对象以推向侧面以尽可能有效地显示目标。在挤压的多边形对象和具有已知长宽比的固定目标的背景下，我们探索了三种横向访问搜索策略：分布面积缩减（DAR），分布熵缩减（DER），利用支持分布和先验信息减少多个时间步的分布和分布熵（DER-MT）。我们使用一阶货架模拟器（FOSS）评估这些策略，在该模型中，我们模拟800个难度不同的随机货架环境，并在具有Fetch机器人和嵌入式PrimeSense RGBD摄像机的物理货架环境中进行仿真。87.3％成功率的平均模拟结果表明，通过2个预测步骤，DER-MT的性能更好。当部署在机器人上时，结果显示所有策略的成功率至少为80％，这表明LAX-RAY可以有效地揭示现实中的目标对象。与在非平凡情况下采用统一概率分布假设的基准策略相比，这两个结果均显示三种拟议策略的性能明显优于基准策略，显示分布预测的重要性。可以在https://sites.google.com/berkeley.edu/lax-ray上找到代码，视频和补充材料。