  Robotic Surgical Assistants (RSAs) are commonly used to perform minimally
invasive surgeries by expert surgeons. However, long procedures filled with
tedious and repetitive tasks such as suturing can lead to surgeon fatigue,
motivating the automation of suturing. As visual tracking of a thin reflective
needle is extremely challenging, prior work has modified the needle with
nonreflective contrasting paint. As a step towards automation of a suturing
subtask without modifying the needle, we propose HOUSTON: Handoff of
Unmodified, Surgical, Tool-Obstructed Needles, a problem and algorithm that
uses a learned active sensing policy with a stereo camera to localize and align
the needle into a visible and accessible pose for the other arm. To compensate
for robot positioning and needle perception errors, the algorithm then executes
a high-precision grasping motion that uses multiple cameras. In physical
experiments using the da Vinci Research Kit (dVRK), HOUSTON successfully passes
unmodified surgical needles with a success rate of 96.7% and is able to perform
handover sequentially between the arms 32.4 times on average before failure. On
needles unseen in training, HOUSTON achieves a success rate of 75 - 92.9%. To
our knowledge, this work is the first to study handover of unmodified surgical
needles. See https://tinyurl.com/houston-surgery for additional materials.

Robotics