Universal Robots ROS1 Control

Collection of utilities for easily controlling Universal Robots either on the Gazebo simulator or the real robots.

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

DOI arXiv Bib PDF Video

@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},
}

Variable compliance control for robotic peg-in-hole assembly: A deep-reinforcement-learning approach

Cristian C Beltran-Hernandez, Damien Petit, Ixchel G Ramirez-Alpizar, and Kensuke Harada

Applied Sciences, 2020

DOI Bib PDF Video

@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},
}