  Recent advances in depth-sensing have significantly increased accuracy,
resolution, and frame rate, as shown in the 1920x1200 resolution and 13 frames
per second Zivid RGBD camera. In this study, we explore the potential of depth
sensing for efficient and reliable automation of surgical subtasks. We consider
a monochrome (all red) version of the peg transfer task from the Fundamentals
of Laparoscopic Surgery training suite implemented with the da Vinci Research
Kit (dVRK). We use calibration techniques that allow the imprecise,
cable-driven da Vinci to reduce error from 4-5 mm to 1-2 mm in the task space.
We report experimental results for a handover-free version of the peg transfer
task, performing 20 and 5 physical episodes with single- and bilateral-arm
setups, respectively. Results over 236 and 49 total block transfer attempts for
the single- and bilateral-arm peg transfer cases suggest that reliability can
be attained with 86.9 % and 78.0 % for each individual block, with respective
block transfer speeds of 10.02 and 5.72 seconds. Supplementary material is
available at https://sites.google.com/view/peg-transfer.
