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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
2017
The international journal of robotics research
We describe a learning-based approach to handeye 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. This requires the network to observe the spatial relationship between the gripper and objects in
doi:10.1177/0278364917710318
fatcat:shisdrnqireejc2zp5zp2u5z4m