  Recent advances in robot-assisted surgery have resulted in progressively more
precise, efficient, and minimally invasive procedures, sparking a new era of
robotic surgical intervention. This enables doctors, in collaborative
interaction with robots, to perform traditional or minimally invasive surgeries
with improved outcomes through smaller incisions. Recent efforts are working
toward making robotic surgery more autonomous which has the potential to reduce
variability of surgical outcomes and reduce complication rates. Deep
reinforcement learning methodologies offer scalable solutions for surgical
automation, but their effectiveness relies on extensive data acquisition due to
the absence of prior knowledge in successfully accomplishing tasks. Due to the
intensive nature of simulated data collection, previous works have focused on
making existing algorithms more efficient. In this work, we focus on making the
simulator more efficient, making training data much more accessible than
previously possible. We introduce Surgical Gym, an open-source high performance
platform for surgical robot learning where both the physics simulation and
reinforcement learning occur directly on the GPU. We demonstrate between
100-5000x faster training times compared with previous surgical learning
platforms. The code is available at:
https://github.com/SamuelSchmidgall/SurgicalGym.
