论文标题
原始形状识别对象抓握
Primitive Shape Recognition for Object Grasping
论文作者
论文摘要
Shape在何处和方式方面都应如何掌握对象。因此，本文介绍了一种基于分割的架构，用于分解用深度摄像头感测到多个原始形状，以及用于机器人抓握的后处理管道。分割采用了一个名为PS-CNN的深网，对合成数据进行了培训，该网络具有6类原始形状，并使用仿真引擎生成。每个原始形状均使用参数化的掌握族设计，从而允许管道识别每个形状区域的多个抓手。 grasps是排序的，第一个可行的grass被选为执行。对于单个对象的无任务掌握，该方法达到了94.2％的成功率，与自上而下和SE（3）基于基于的方法相比，将其放置在最高的掌握方法中。涉及变量观点和混乱的其他测试表明了设置的鲁棒性。对于以任务为导向的握力，PS-CNN取得了93.0％的成功率。总体而言，结果支持以下假设：在握把管道中明确编码形状原始图应提高抓地力的性能，包括无任务和与任务相关的掌握预测。
Shape informs how an object should be grasped, both in terms of where and how. As such, this paper describes a segmentation-based architecture for decomposing objects sensed with a depth camera into multiple primitive shapes, along with a post-processing pipeline for robotic grasping. Segmentation employs a deep network, called PS-CNN, trained on synthetic data with 6 classes of primitive shapes and generated using a simulation engine. Each primitive shape is designed with parametrized grasp families, permitting the pipeline to identify multiple grasp candidates per shape region. The grasps are rank ordered, with the first feasible one chosen for execution. For task-free grasping of individual objects, the method achieves a 94.2% success rate placing it amongst the top performing grasp methods when compared to top-down and SE(3)-based approaches. Additional tests involving variable viewpoints and clutter demonstrate robustness to setup. For task-oriented grasping, PS-CNN achieves a 93.0% success rate. Overall, the outcomes support the hypothesis that explicitly encoding shape primitives within a grasping pipeline should boost grasping performance, including task-free and task-relevant grasp prediction.
