GQSTN: Optimizing OneShot Grasp Detection based on Robustness Classifier Grasping is a fundamental robotic task needed for the deployment of household robots or furthering warehouse automation. However, few approaches are able to perform grasp detection in real time (frame rate). To this effect, we present Grasp Quality Spatial Transformer Network (GQSTN), a oneshot grasp detection network. Being based on the Spatial Transformer Network (STN), it produces not only a grasp configuration, but also directly outputs a depth image centered at this configuration. By connecting our architecture to an externallytrained grasp robustness evaluation network, we can train efficiently to satisfy a robustness metric via the backpropagation of the gradient emanating from the evaluation network. This removes the difficulty of training detection networks on sparsely annotated databases, a common issue in grasping. We further propose to use this robustness classifier to compare approaches, being more reliable than the traditional rectangle metric. Our GQSTN is able to detect robust grasps on the depth images of the DexNet 2.0 dataset with 92.4 % accuracy in a single pass of the network. We finally demonstrate in a physical benchmark that our method can propose robust grasps more often than previous samplingbased methods, while being more than 60 times faster.

A Cascaded Deep Learning Framework for Realtime and Robust Grasp Planning * Robotic grasping has been regarded as an essential way for robots to interact with the environment, which makes it a research hotspot. To our knowledge, current work on grasp planning could rarely take both robustness and time efficiency into account. To solve this problem, we propose a cascaded deep learning framework, which consists of a regression CNN and a refined CNN. Taking depth image as input, the first network regresses an optimal grasp region. The second network samples and sorts grasp candidates in the region and finally outputs a series of grasp poses with high quality. This architecture ensures realtime performance and acquisition of adequate candidates synchronously, which is especially appropriate for unstructured environment manipulation. We used Gazebo simulation and DART physics engines to build a grasp data generation environment. Comparing with manually annotating or robot collecting, our method makes data generation less expensive, more dynamic, and easier to generalize into the real world. The method proposed is evaluated in both simulation and physical experiments, verifying that our method is able to find multiple robuster grasp poses at a faster rate than the state of the art.