This can be achieved by off-the-shelf vision-based solutions, but most require prior knowledge about each object to be manipulated. To increase flexibility, an industrial robot should be able to adjust its configuration in order to grasp objects in variable/unknown positions. In many cases, the movements of industrial robot manipulators are hard-coded or manually defined, and need to be adjusted if the objects being manipulated change position. Results show that the best performance in our application was reached by MobileNet, with an average of 84 % accuracy after training in simulated environment.ĪB - Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. Experiments were carried out in a simulated environment to compare the performance of four different pre-trained CNN models (RexNext, MobileNet, MNASNet and DenseNet). The Q-values are estimated using Convolutional Neural Network (CNN) based on pre-trained models for feature extraction. The solution uses deep Q-learning to process the color and depth images and generate a 휖 -greedy policy used to define the robot action. To address this issue, this work presents a ROS-based deep reinforcement learning solution to robotic grasping for a Collaborative Robot (Cobot) using a depth camera. N2 - Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. T1 - Deep Reinforcement Learning Applied to a Robotic Pick-and-Place Application Results show that the best performance in our application was reached by MobileNet, with an average of 84 % accuracy after training in simulated environment.',

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Results show that the best performance in our application was reached by MobileNet, with an average of 84 % accuracy after training in simulated environment.Ībstract = 'Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. The solution uses deep Q-learning to process the color and depth images and generate a 𝜖 -greedy policy used to define the robot action.

Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place.

Pick and learn