  We consider a micromanipulation problem in eye surgery, specifically retinal
vein cannulation (RVC). RVC involves inserting a microneedle into a retinal
vein for the purpose of targeted drug delivery. The procedure requires
accurately guiding a needle to a target vein and inserting it while avoiding
damage to the surrounding tissues. RVC can be considered similar to the reach
or push task studied in robotics manipulation, but with additional constraints
related to precision and safety while interacting with soft tissues. Prior
works have mainly focused developing robotic hardware and sensors to enhance
the surgeons' accuracy, leaving the automation of RVC largely unexplored. In
this paper, we present the first autonomous strategy for RVC while relying on a
minimal setup: a robotic arm, a needle, and monocular images. Our system
exclusively relies on monocular vision to achieve precise navigation, gentle
placement on the target vein, and safe insertion without causing tissue damage.
Throughout the procedure, we employ machine learning for perception and to
identify key surgical events such as needle-vein contact and vein punctures.
Detecting these events guides our task and motion planning framework, which
generates safe trajectories using model predictive control to complete the
procedure. We validate our system through 24 successful autonomous trials on 4
cadaveric pig eyes. We show that our system can navigate to target veins within
22 micrometers of XY accuracy and under 35 seconds, and consistently puncture
the target vein without causing tissue damage. Preliminary comparison to a
human demonstrates the superior accuracy and reliability of our system.
