In scenarios involving the grasping of multiple targets, the learning of stacking relationships between objects is fundamental for robots to execute safely and efficiently. However, current methods lack subdivision for the hierarchy of stacking relationship types. In scenes where objects are mostly stacked in an orderly manner, they are incapable of performing human-like and high-efficient grasping decisions. This paper proposes a perception-planning method to distinguish different stacking types between objects and generate prioritized manipulation order decisions based on given target designations. We utilize a Hierarchical Stacking Relationship Network (HSRN) to discriminate the hierarchy of stacking and generate a refined Stacking Relationship Tree (SRT) for relationship description. Considering that objects with high stacking stability can be grasped together if necessary, we introduce an elaborate decision-making planner based on the Partially Observable Markov Decision Process (POMDP), which leverages observations and generates the least grasp-consuming decision chain with robustness and is suitable for simultaneously specifying multiple targets. To verify our work, we set the scene to the dining table and augment the REGRAD dataset with a set of common tableware models for network training. Experiments show that our method effectively generates grasping decisions that conform to human requirements, and improves the implementation efficiency compared with existing methods on the basis of guaranteeing the success rate.

翻译：在涉及多目标抓取场景中，物体间堆叠关系的学习是机器人安全高效执行任务的基础。然而现有方法对堆叠关系类型的层级划分不够精细，在物体大多有序堆叠的场景中，无法执行类人化且高时效的抓取决策。本文提出一种感知-规划方法，用于区分物体间的不同堆叠类型，并基于给定目标指定生成优先级操作顺序决策。我们利用分层堆叠关系网络（HSRN）判别堆叠层级结构，并生成精细化的堆叠关系树（SRT）用于关系描述。考虑到高稳定性堆叠的物体在必要时可被同时抓取，我们引入基于部分可观测马尔可夫决策过程（POMDP）的精细决策规划器，该规划器利用观测信息生成具有鲁棒性的最少抓取消耗决策链，且适用于同时指定多个目标的场景。为验证工作有效性，我们将场景设定为餐桌，并用一组常见餐具模型扩充REGRAD数据集进行网络训练。实验表明，本方法能有效生成符合人类需求的抓取决策，并在保证成功率的基础上相较现有方法提升了实施效率。