Surgical robotics is a rapidly evolving field that is transforming the landscape of surgeries. Surgical robots have been shown to enhance precision, minimize invasiveness, and alleviate surgeon fatigue. One promising area of research in surgical robotics is the use of reinforcement learning to enhance the automation level. Reinforcement learning is a type of machine learning that involves training an agent to make decisions based on rewards and punishments. This literature review aims to comprehensively analyze existing research on reinforcement learning in surgical robotics. The review identified various applications of reinforcement learning in surgical robotics, including pre-operative, intra-body, and percutaneous procedures, listed the typical studies, and compared their methodologies and results. The findings show that reinforcement learning has great potential to improve the autonomy of surgical robots. Reinforcement learning can teach robots to perform complex surgical tasks, such as suturing and tissue manipulation. It can also improve the accuracy and precision of surgical robots, making them more effective at performing surgeries.

翻译：手术机器人是一个快速发展的领域，正在重塑外科手术的面貌。已有研究表明，手术机器人能够增强手术精度、减少创伤性并减轻外科医生的疲劳。手术机器人领域一个具有前景的研究方向是利用强化学习提升自动化水平。强化学习是一种机器学习方法，通过基于奖励和惩罚训练智能体进行决策。本文献综述旨在全面分析手术机器人中强化学习的现有研究。综述识别了强化学习在手术机器人中的多种应用，包括术前规划、体内手术及经皮穿刺等操作，列举了代表性研究，并比较了其方法与结果。研究结果表明，强化学习在提升手术机器人自主性方面具有巨大潜力。强化学习可教会机器人执行复杂的操作任务，如缝合和组织操作；同时还能提高手术机器人的准确性与精密度，使其在执行手术时更加高效。