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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.
Mar 7, 2016
The work presented in this paper shows how the association of proprioceptive and exteroceptive stimuli can enable a Kohonen neural network, controlling a ...
The aim of the work presented in this paper is to show how the association of proprioceptive and exteroceptive stimuli can enable a neural network, controlling ...
The work presented in this paper shows how the association of proprioceptive and exteroceptive stimuli can enable a Kohonen neural network, controlling a ...
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A neural network model has been developed that achieves adaptive visual-motor coordination of a multijoint arm, without a teacher.
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This paper describes a neural network approach to visual servoing for the control of robot arm movements. The method is based solely on visual feedback, ...
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 ...
Using neural networks to learn hand-eye co-ordination. https://doi.org/10.1007/bf01423095 ·. Journal: Neural Computing & Applications, 1994, № 1, p. 2-12.
Shows that a robot with the hand-eye system can learn the hand-reaching task without apparent calculations of the target, obstacle, and hand locations.
This paper focuses on learning hand-eye coordination on robot NAO. It elaborates on two different approaches for computing inverse kinematics using neural ...