Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots
This work addresses the need for efficient data collection in surgical automation, potentially reducing variability and complications in robotic surgery, though it is incremental as it focuses on simulator optimization rather than new algorithms.
The authors tackled the problem of slow training times for deep reinforcement learning in surgical robotics by introducing Surgical Gym, a GPU-based platform that accelerates physics simulation and RL training, achieving 100-5000x faster training times compared to previous platforms.
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.