HomographyBased Deep Visual Servoing Methods for Planar Grasps We propose a visual servoing framework for learning to improve grasps of objects. RGB and depth images from grasp attempts are collected using an automated data collection process. The data is then used to train a Grasp Quality Network (GQN) that predicts the outcome of grasps from visual information. A grasp optimization pipeline uses homography models with the trained network to optimize the grasp success rate. We evaluate and compare several algorithms for adjusting the current gripper pose based on the current observation from a grippermounted camera to perform visual servoing. Evaluations in both simulated and hardware environments show considerable improvement in grasp robustness with models trained using less than 30K grasp trials. Success rates for grasping novel objects unseen during training increased from 18.5% to 81.0% in simulation, and from 17.8% to 78.0% in the real world.

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.