  Tactile information is important for robust performance in robotic tasks that
involve physical interaction, such as object manipulation. However, with more
data included in the reasoning and control process, modeling behavior becomes
increasingly difficult. Deep Reinforcement Learning (DRL) produced promising
results for learning complex behavior in various domains, including
tactile-based manipulation in robotics. In this work, we present our
open-source reinforcement learning environments for the TIAGo service robot.
They produce tactile sensor measurements that resemble those of a real
sensorised gripper for TIAGo, encouraging research in transfer learning of DRL
policies. Lastly, we show preliminary training results of a learned force
control policy and compare it to a classical PI controller.
