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 References 2020 IROS + RAL Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robots
Cristian Camilo Beltran-Hernandez , Damien Petit , Ixchel Georgina Ramirez-Alpizar, Takayuki Nishi, Shinichi Kikuchi, and 2 more authors
IEEE Robotics and Automation Letters , 2020
@article { beltran2020learning ,
title = {Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robots} ,
author = {Beltran-Hernandez, Cristian Camilo and Petit, Damien and Ramirez-Alpizar, Ixchel Georgina and Nishi, Takayuki and Kikuchi, Shinichi and Matsubara, Takamitsu and Harada, Kensuke} ,
journal = {IEEE Robotics and Automation Letters} ,
volume = {5} ,
number = {4} ,
pages = {5709--5716} ,
year = {2020} ,
publisher = {IEEE} ,
doi = {10.1109/LRA.2020.3010739} ,
dimensions = {true}
}
Variable compliance control for robotic peg-in-hole assembly: A deep-reinforcement-learning approach
Applied Sciences , 2020
@article { beltran2020variable ,
title = {Variable compliance control for robotic peg-in-hole assembly: A deep-reinforcement-learning approach} ,
author = {Beltran-Hernandez, Cristian C and Petit, Damien and Ramirez-Alpizar, Ixchel G and Harada, Kensuke} ,
journal = {Applied Sciences} ,
volume = {10} ,
number = {19} ,
pages = {6923} ,
year = {2020} ,
publisher = {MDPI} ,
doi = {10.3390/app10196923} ,
}