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

7 Mar 2016Sergey LevinePeter PastorAlex KrizhevskyDeirdre Quillen

We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose... (read more)

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