  Reinforcement Learning (RL) methods have been widely applied for robotic
manipulations via sim-to-real transfer, typically with proprioceptive and
visual information. However, the incorporation of tactile sensing into RL for
contact-rich tasks lacks investigation. In this paper, we model a tactile
sensor in simulation and study the effects of its feedback in RL-based robotic
control via a zero-shot sim-to-real approach with domain randomization. We
demonstrate that learning and controlling with feedback from tactile sensor
arrays at the gripper, both in simulation and reality, can enhance grasping
stability, which leads to a significant improvement in robotic manipulation
performance for a door opening task. In real-world experiments, the door open
angle was increased by 45% on average for transferred policies with tactile
sensing over those without it.
