Publication
Learning Tactile Insertion in the Real World
D. Palenicek; T. Gruner; T. Schneider; A. Böhm; J. Lenz; I. Pfenning; E. Krämer; Jan Peters
In: 40th Anniversary of the IEEE International Conference on Robotics and Automation (ICRA@40). IEEE International Conference on Robotics and Automation (ICRA-2024), IEEE, 2024.
Abstract
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. But, 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
initial results show that Dreamer is capable of utilizing tactile
inputs to solve robotic manipulation tasks in simulation and
reality. Further, 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.