VisualGuided Robot Arm Using MultiTask Faster RCNN The limitation of current visual recognition methods is a big obstacle for the application of automated robot arm systems into industrial projects, which require high precision and speed. In this work, we present a Faster RCNN based multitask network, a deep neural network model, that is able to simultaneously perform three tasks including object detection, category classification and object angle estimation. Afterward, the outputs of all three tasks are utilized to decide a picking point and a rotated gripper angle for the pickandplace robot arm system. The test results show that our network achieves a mean average precision of 86.6% at IoU (Intersection over Union) of 0.7, and a mean accuracy of 83.5% for the final prediction including object localization and angle estimation. In addition, the proposed multitask network takes approximately 0.072 seconds to process an image, which is acceptable for pickandplace robot arms.

A Multitask Convolutional Neural Network for Autonomous Robotic Grasping in Object Stacking Scenes Autonomous robotic grasping plays an important role in intelligent robotics. However, how to help the robot grasp specific objects in object stacking scenes is still an open problem, because there are two main challenges for autonomous robots: (1) it is a comprehensive task to know what and how to grasp; (2) it is hard to deal with the situations in which the target is hidden or covered by other objects. In this paper, we propose a multitask convolutional neural network for autonomous robotic grasping, which can help the robot find the target, make the plan for grasping and finally grasp the target step by step in object stacking scenes. We integrate visionbased robotic grasping detection and visual manipulation relationship reasoning in one single deep network and build the autonomous robotic grasping system. Experimental results demonstrate that with our model, Baxter robot can autonomously grasp the target with a success rate of 90.6%, 71.9% and 59.4% in object cluttered scenes, familiar stacking scenes and complex stacking scenes respectively.