As robots become more widely available outside industrial settings, the need for reliable object grasping and manipulation is increasing. In such environments, robots must be able to grasp and manipulate novel objects in various situations. This paper presents GraspCaps, a novel architecture based on Capsule Networks for generating per-point 6D grasp configurations for familiar objects. GraspCaps extracts a rich feature vector of the objects present in the point cloud input, which is then used to generate per-point grasp vectors. This approach allows the network to learn specific grasping strategies for each object category. In addition to GraspCaps, the paper also presents a method for generating a large object-grasping dataset using simulated annealing. The obtained dataset is then used to train the GraspCaps network. Through extensive experiments, we evaluate the performance of the proposed approach, particularly in terms of the success rate of grasping familiar objects in challenging real and simulated scenarios. The experimental results showed that the overall object-grasping performance of the proposed approach is significantly better than the selected baseline. This superior performance highlights the effectiveness of the GraspCaps in achieving successful object grasping across various scenarios.

翻译：随着机器人在工业环境之外的广泛应用，对可靠物体抓取与操作的需求日益增长。在这类环境中，机器人必须能够在各种场景下抓取和操作新物体。本文提出GraspCaps——一种基于胶囊网络的新型架构，用于为熟悉物体生成逐点六自由度抓取配置。GraspCaps从输入的点云中提取场景内物体的丰富特征向量，进而生成逐点抓取向量。该方法使网络能够学习每个物体类别的特定抓取策略。除GraspCaps外，本文还提出一种利用模拟退火生成大规模物体抓取数据集的方案，并利用该数据集训练GraspCaps网络。通过大量实验，我们评估了所提方法的性能，特别是其在具有挑战性的真实和模拟场景中抓取熟悉物体的成功率。实验结果表明，相较于所选基线方法，所提方法在整体物体抓取性能上具有显著优势。这一优异表现凸显了GraspCaps在多种场景下实现成功物体抓取的有效性。