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 visionbased
solutions, but most require prior knowledge about each object to
be 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 ϵ-
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 pretrained
CNN models (RexNext, MobileNet, MNASNet and DenseNet).
Results show that the best performance in our application was reached by
MobileNet, with an average of 84 % accuracy after training in simulated
environment.
This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020 and by the Innovation Cluster Dracten (ICD), project Collaborative Connected Robots (Cobots) 2.0.
The authors also thank the support from the Research Centre Biobased Economy from
the Hanze University of Applied Sciences.
