Article intro - Intermittent Visual Servoing
Abstract
"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."
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