  Peg transfer is a well-known surgical training task in the Fundamentals of
Laparoscopic Surgery (FLS). While human sur-geons teleoperate robots such as
the da Vinci to perform this task with high speed and accuracy, it is
challenging to automate. This paper presents a novel system and control method
using a da Vinci Research Kit (dVRK) surgical robot and a Zivid depth sensor,
and a human subjects study comparing performance on three variants of the
peg-transfer task: unilateral, bilateral without handovers, and bilateral with
handovers. The system combines 3D printing, depth sensing, and deep learning
for calibration with a new analytic inverse kinematics model and a
time-minimized motion controller. In a controlled study of 3384 peg transfer
trials performed by the system, an expert surgical resident, and 9 volunteers,
results suggest that the system achieves accuracy on par with the experienced
surgical resident and is significantly faster and more consistent than the
surgical resident and volunteers. The system also exhibits the highest
consistency and lowest collision rate. To our knowledge, this is the first
autonomous system to achieve superhuman performance on a standardized surgical
task.
