Grasping specific objects in complex and irregularly stacked scenes is still challenging for robotics. Because the robot is not only required to identify the object's grasping posture but also needs to reason the manipulation relationship between the objects. In this paper, we propose a manipulation relationship reasoning network with a multi-scale feature aggregation (MSFA) mechanism for robot grasping tasks. MSFA aggregates high-level semantic information and low-level spatial information in a cross-scale connection way to improve the generalization ability of the model. Furthermore, to improve the accuracy, we propose to use intersection features with rich location priors for manipulation relationship reasoning. Experiments are validated in VMRD datasets and real environments, respectively. The experimental results demonstrate that our proposed method can accurately predict the manipulation relationship between objects in the scene of multi-object stacking. Compared with previous methods, it significantly improves reasoning speed and accuracy.

翻译：在复杂且不规则堆叠场景中抓取特定物体对机器人仍具挑战性。因为机器人不仅需要识别物体的抓取姿态，还需推理物体间的操作关系。本文提出一种具备多尺度特征聚合机制的操作关系推理网络，用于机器人抓取任务。多尺度特征聚合通过跨尺度连接方式聚合高层语义信息与低层空间信息，提升模型泛化能力。此外，为提升精度，我们提出利用富含位置先验的交集特征进行操作关系推理。实验分别在VMRD数据集和真实环境中进行验证。结果表明，所提方法能准确预测多物体堆叠场景中物体间的操作关系，与现有方法相比，显著提升了推理速度与精度。