Deep Reinforcement Learning Applied to a Robotic Pick-and-Place Application

Felipe Martins, Heinrich Wörtche, Natanael Gomes (First author), José Lima

Onderzoeksoutput: PaperAcademic

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Samenvatting

Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. 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. To increase flexibility, an industrial robot should be able to adjust its configuration in order to grasp objects in variable/unknown positions. This can be achieved by off-the-shelf vision-based solutions, but most require prior knowledge about each object tobe manipulated. 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. The solution uses deep Q-learning to process the color and depth images and generate a greedy policy used to define the robot action. The Q-values are estimated using Convolutional Neural Network (CNN) based on pre-trained models for feature extraction. Experiments were carried out in a simulated environment to compare the performance of four different pre-trained CNNmodels (RexNext, MobileNet, MNASNet and DenseNet). Results showthat the best performance in our application was reached by MobileNet,with an average of 84 % accuracy after training in simulated environment.
Originele taal-2English
Aantal pagina's16
StatusAccepted/In press - 19 jul. 2021
EvenementOL2A: International Conference on
Optimization, Learning Algorithms and Applications
- On-line
Duur: 19 jul. 202121 jul. 2021
http://ol2a.ipb.pt/EN_index.html

Conference

ConferenceOL2A: International Conference on
Optimization, Learning Algorithms and Applications
Periode19/07/2121/07/21
Internet adres

Keywords

  • grijpen
  • cobots
  • reinforcement learning
  • zelflerende systemen
  • pick-and-place

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