SonoGym: High Performance Simulation for Challenging Surgical Tasks with Robotic Ultrasound
Journal:
arXiv
Published Date:
Jul 1, 2025
Abstract
Ultrasound (US) is a widely used medical imaging modality due to its
real-time capabilities, non-invasive nature, and cost-effectiveness. Robotic
ultrasound can further enhance its utility by reducing operator dependence and
improving access to complex anatomical regions. For this, while deep
reinforcement learning (DRL) and imitation learning (IL) have shown potential
for autonomous navigation, their use in complex surgical tasks such as anatomy
reconstruction and surgical guidance remains limited -- largely due to the lack
of realistic and efficient simulation environments tailored to these tasks. We
introduce SonoGym, a scalable simulation platform for complex robotic
ultrasound tasks that enables parallel simulation across tens to hundreds of
environments. Our framework supports realistic and real-time simulation of US
data from CT-derived 3D models of the anatomy through both a physics-based and
a generative modeling approach. Sonogym enables the training of DRL and recent
IL agents (vision transformers and diffusion policies) for relevant tasks in
robotic orthopedic surgery by integrating common robotic platforms and
orthopedic end effectors. We further incorporate submodular DRL -- a recent
method that handles history-dependent rewards -- for anatomy reconstruction and
safe reinforcement learning for surgery. Our results demonstrate successful
policy learning across a range of scenarios, while also highlighting the
limitations of current methods in clinically relevant environments. We believe
our simulation can facilitate research in robot learning approaches for such
challenging robotic surgery applications. Dataset, codes, and videos are
publicly available at https://sonogym.github.io/.
