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使得手术过程日益精准、高效且微创，开启了机器人手术干预的新纪元。这使得医生能够在与机器人协同合作下，通过更小的切口完成传统或微创手术，从而改善手术效果。近期研究致力于提升机器人手术的自主性，这有望降低手术结果的差异性并减少并发症发生率。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的先验知识，其有效性依赖于大量数据采集。受限于模拟数据采集的高强度特性，先前的研究主要聚焦于提升现有算法的效率。本工作则致力于提升模拟器的效率，使训练数据的获取比以往更加便捷。我们推出了Surgical Gym——一个面向手术机器人学习的开源高性能平台，其物理模拟与强化学习过程均在GPU上直接运行。实验表明，与既往手术学习平台相比，本平台的训练速度提升了100-5000倍。代码开源地址为：https://github.com/SamuelSchmidgall/SurgicalGym。