VisionBased Grasping and Manipulation of Flexible USB Wires The USB wires are commonly used in many 3C products (Computer, Communication and Consumer electronics), such as smartphones, laptops, and tablets. The demand for manufacturing and soldering USB wires is now increasing significantly as the rapidly expanding 3C market However, because of the small size and the deformation property of USB wires, current soldering procedures are heavily dependent on manual works. There are three steps to solder the USB wires: Separating, Sorting, and Pressing. In this paper, it mainly deals with wire sorting and pressing tasks after the wire separation. Vision feedback is applied to the sorting task and it also assesses the status of completion. The developed grasping and manipulation algorithm have the advantage of highly autonomous capability, in sense that no human assistance or supervision is required throughout the procedure. Moreover, it has a certain level of intelligence, the actual operation sequence is not completely fixed, it is based on the principle of the minimum distance between the position of the grasping point and the position of soldering groove, which will improve the overall efficiency.

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.