  Automation holds the potential to assist surgeons in robotic interventions,
shifting their mental work load from visuomotor control to high level decision
making. Reinforcement learning has shown promising results in learning complex
visuomotor policies, especially in simulation environments where many samples
can be collected at low cost. A core challenge is learning policies in
simulation that can be deployed in the real world, thereby overcoming the
sim-to-real gap.
  In this work, we bridge the visual sim-to-real gap with an image-based
reinforcement learning pipeline based on pixel-level domain adaptation and
demonstrate its effectiveness on an image-based task in deformable object
manipulation. We choose a tissue retraction task because of its importance in
clinical reality of precise cancer surgery. After training in simulation on
domain-translated images, our policy requires no retraining to perform tissue
retraction with a 50% success rate on the real robotic system using raw RGB
images. Furthermore, our sim-to-real transfer method makes no assumptions on
the task itself and requires no paired images. This work introduces the first
successful application of visual sim-to-real transfer for robotic manipulation
of deformable objects in the surgical field, which represents a notable step
towards the clinical translation of cognitive surgical robotics.
