Despite advancements in robotic-assisted surgery, automating complex tasks like suturing remain challenging due to the need for adaptability and precision. Learning-based approaches, particularly reinforcement learning (RL) and imitation learning (IL), require realistic simulation environments for efficient data collection. However, current platforms often include only relatively simple, non-dexterous manipulations and lack the flexibility required for effective learning and generalization. We introduce SurgicAI, a novel platform for development and benchmarking addressing these challenges by providing the flexibility to accommodate both modular subtasks and more importantly task decomposition in RL-based surgical robotics. Compatible with the da Vinci Surgical System, SurgicAI offers a standardized pipeline for collecting and utilizing expert demonstrations. It supports deployment of multiple RL and IL approaches, and the training of both singular and compositional subtasks in suturing scenarios, featuring high dexterity and modularization. Meanwhile, SurgicAI sets clear metrics and benchmarks for the assessment of learned policies. We implemented and evaluated multiple RL and IL algorithms on SurgicAI. Our detailed benchmark analysis underscores SurgicAI's potential to advance policy learning in surgical robotics. Details: \url{https://github.com/surgical-robotics-ai/SurgicAI

翻译：尽管机器人辅助手术取得了进展，但由于需要适应性和精确性，自动化缝合等复杂任务仍然具有挑战性。基于学习的方法，特别是强化学习（RL）和模仿学习（IL），需要逼真的仿真环境以进行高效的数据收集。然而，现有平台通常只包含相对简单、非灵巧的操作，缺乏有效学习和泛化所需的灵活性。我们提出了SurgicAI，这是一个用于开发和基准测试的新颖平台，通过提供灵活性以适应模块化子任务，更重要的是适应基于RL的手术机器人学中的任务分解，从而应对这些挑战。SurgicAI与达芬奇手术系统兼容，提供了一个用于收集和利用专家示范的标准化流程。它支持部署多种RL和IL方法，并支持在缝合场景中训练单一和组合子任务，具有高灵巧性和模块化特点。同时，SurgicAI为评估学习到的策略设定了清晰的指标和基准。我们在SurgicAI上实现并评估了多种RL和IL算法。我们详细的基准分析凸显了SurgicAI在推进手术机器人学策略学习方面的潜力。详情：\url{https://github.com/surgical-robotics-ai/SurgicAI}