  Automation of surgical tasks using cable-driven robots is challenging due to
backlash, hysteresis, and cable tension, and these issues are exacerbated as
surgical instruments must often be changed during an operation. In this work,
we propose a framework for automation of high-precision surgical tasks by
learning sample efficient, accurate, closed-loop policies that operate directly
on visual feedback instead of robot encoder estimates. This framework, which we
call intermittent visual servoing (IVS), intermittently switches to a learned
visual servo policy for high-precision segments of repetitive surgical tasks
while relying on a coarse open-loop policy for the segments where precision is
not necessary. To compensate for cable-related effects, we apply imitation
learning to rapidly train a policy that maps images of the workspace and
instrument from a top-down RGB camera to small corrective motions. We train the
policy using only 180 human demonstrations that are roughly 2 seconds each.
Results on a da Vinci Research Kit suggest that combining the coarse policy
with half a second of corrections from the learned policy during each
high-precision segment improves the success rate on the Fundamentals of
Laparoscopic Surgery peg transfer task from 72.9% to 99.2%, 31.3% to 99.2%, and
47.2% to 100.0% for 3 instruments with differing cable-related effects. In the
contexts we studied, IVS attains the highest published success rates for
automated surgical peg transfer and is significantly more reliable than
previous techniques when instruments are changed. Supplementary material is
available at https://tinyurl.com/ivs-icra.
