Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots

TL;DR

Surgical Gym is a GPU-based platform that significantly accelerates reinforcement learning for surgical robots by enabling physics simulation and training directly on the GPU, reducing training times by up to 5000 times.

Contribution

This work introduces Surgical Gym, a high-performance, open-source platform that makes surgical robot training more efficient by leveraging GPU acceleration for simulation and learning.

Findings

Achieves 100-5000x faster training times than previous platforms.

Enables scalable and efficient surgical robot reinforcement learning.

Open-source code available for community use.

Abstract

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.