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We describe a learning-based approach to hand-eye coordination for roboticgrasping from monocular images. To learn hand-eye coordination for grasping, wetrained a large convolutional neural network to predict the probability thattask-space motion of the gripper will result in successful grasps, using onlymonocular camera images and independently of camera calibration or the currentrobot pose. This requires the network to observe the spatial relationshipbetween the gripper and objects in the scene, thus learning hand-eyecoordination. We then use this network to servo the gripper in real time toachieve successful grasps. To train our network, we collected over 800,000grasp attempts over the course of two months, using between 6 and 14 roboticmanipulators at any given time, with differences in camera placement andhardware. Our experimental evaluation demonstrates that our method achieveseffective real-time control, can successfully grasp novel objects, and correctsmistakes by continuous servoing.

Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection