Simultion environment for Universal Robot arms, used for research project to learn contact-rich manipulation tasks. The default set up uses UR3e but it can be adapted to any Universal Robot arm.
See public repositories here:
UR3 + Robotiq 85 gripper
UR3e + Robotiq Hand-e gripper
Training session with domain randomization
Retraining. Learning directly on real hardware
Variable compliance control for robotic peg-in-hole assembly: A deep-reinforcement-learning approachApplied Sciences, 2020
Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled RobotsIEEE Robotics and Automation Letters, 2020