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.

翻译：机器人辅助手术的最新进展推动了手术过程日益精准、高效和微创，开启了机器人手术干预的新纪元。这使得医生能够通过与机器人协作配合，以更小的切口完成传统或微创手术，从而获得更好的治疗效果。近期研究致力于提高机器人手术的自主性，这有望减少手术结果的差异并降低并发症发生率。深度强化学习方法为手术自动化提供了可扩展的解决方案，但由于缺乏成功完成任务所需的先验知识，其有效性依赖于大量数据采集。鉴于模拟数据收集的高强度特性，以往研究主要聚焦于提升现有算法的效率。本研究则重点关注提升模拟器的效率，使训练数据的获取比以往更加便捷。我们提出了Surgical Gym——一个用于手术机器人学习的开源高性能平台，其中物理模拟和强化学习均直接在GPU上运行。实验表明，与以往的手术学习平台相比，训练速度提升了100到5000倍。相关代码已开源至：https://github.com/SamuelSchmidgall/SurgicalGym。