  Humans have exceptional tactile sensing capabilities, which they can leverage
to solve challenging, partially observable tasks that cannot be solved from
visual observation alone. Research in tactile sensing attempts to unlock this
new input modality for robots. Lately, these sensors have become cheaper and,
thus, widely available. At the same time, the question of how to integrate them
into control loops is still an active area of research, with central challenges
being partial observability and the contact-rich nature of manipulation tasks.
In this study, we propose to use Reinforcement Learning to learn an end-to-end
policy, mapping directly from tactile sensor readings to actions. Specifically,
we use Dreamer-v3 on a challenging, partially observable robotic insertion task
with a Franka Research 3, both in simulation and on a real system. For the real
setup, we built a robotic platform capable of resetting itself fully
autonomously, allowing for extensive training runs without human supervision.
Our preliminary results indicate that Dreamer is capable of utilizing tactile
inputs to solve robotic manipulation tasks in simulation and reality.
Furthermore, we find that providing the robot with tactile feedback generally
improves task performance, though, in our setup, we do not yet include other
sensing modalities. In the future, we plan to utilize our platform to evaluate
a wide range of other Reinforcement Learning algorithms on tactile tasks.
