标题： SonoGym：具有机器人超声的挑战性外科任务的高性能模拟 显示英文标题

标题： SonoGym: High Performance Simulation for Challenging Surgical Tasks with Robotic Ultrasound

摘要： 超声（US）由于其实时能力、非侵入性和成本效益，是一种广泛使用的医学成像方式。 通过减少操作员依赖性和提高对复杂解剖区域的访问性，机器人超声可以进一步增强其效用。 为此，尽管深度强化学习（DRL）和模仿学习（IL）在自主导航方面显示出潜力，但它们在复杂的手术任务（如解剖结构重建和手术引导）中的应用仍然有限——这主要是由于缺乏针对这些任务的现实且高效的模拟环境。 我们引入了SonoGym，一个可扩展的模拟平台，用于复杂的机器人超声任务，该平台能够在数十到数百个环境中进行并行模拟。 我们的框架通过基于物理的方法和生成建模方法，支持从CT衍生的解剖三维模型中进行真实且实时的超声数据模拟。 SonoGym通过集成常见的机器人平台和骨科末端执行器，使DRL和最近的IL代理（视觉变压器和扩散策略）能够训练相关任务，如机器人骨科手术。 我们进一步结合了子模DRL——一种处理历史相关奖励的最新方法——用于解剖结构重建和手术的安全强化学习。 我们的结果展示了在各种场景中成功的策略学习，同时也突显了当前方法在临床相关环境中的局限性。 我们认为我们的模拟可以促进此类具有挑战性的机器人手术应用中机器人学习方法的研究。 数据集、代码和视频可在https://sonogym.github.io/上公开获取。 显示英文摘要

摘要： 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/.