Nowadays, robots are heavily used in factories for different tasks, most of them including grasping and manipulation of generic objects in unstructured scenarios. In order to better mimic a human operator involved in a grasping action, where he/she needs to identify the object and detect an optimal grasp by means of visual information, a widely adopted sensing solution is Artificial Vision. Nonetheless, state-of-art applications need long training and fine-tuning for manually build the object’s model that is used at run-time during the normal operations, which reduce the overall operational throughput of the robotic system. To overcome such limits, the paper presents a framework based on Deep Convolutional Neural Networks (DCNN) to predict both single and multiple grasp poses for multiple objects all at once, using a single RGB image as input. Thanks to a novel loss function, our framework is trained in an end-to-end fashion and matches state-of-art accuracy with a substantially smaller architecture, which gives unprecedented real-time performances during experimental tests, and makes the application reliable for working on real robots. The system has been implemented using the ROS framework and tested on a Baxter collaborative robot.

如今，机器人在工厂中大量使用，以完成不同的任务，其中大多数包括在非结构化场景中抓握和操纵通用对象。为了更好地模仿参与抓握动作的操作人员，在该操作中他/她需要通过视觉信息识别物体并检测最佳抓握，因此广泛采用的传感解决方案是“人工视觉”。但是，最新的应用程序需要经过长期的培训和微调，才能手动构建正常运行期间在运行时使用的对象模型，从而降低了机器人系统的总体运行吞吐量。为了克服这些限制，本文提出了一个基于深度卷积神经网络（DCNN）的框架，使用单个RGB图像作为输入，可以一次预测多个对象的单个和多个抓握姿势。得益于新颖的损失函数，我们的框架以端到端的方式进行了培训，并以较小的体系结构与最新的准确性相匹配，从而在实验测试中提供了前所未有的实时性能，并使应用程序对于在真正的机器人上工作。该系统已使用ROS框架实现，并在百特协作机器人上进行了测试。