2020
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, Takamitsu Matsubara +1
RA-L 2020
Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because working with real hardware entails additional challenges, especially when using rigid position-controlled manipulators.