Surgical robot automation has attracted increasing research interest over the past decade, expecting its huge potential to benefit surgeons, nurses and patients. Recently, the learning paradigm of embodied AI has demonstrated promising ability to learn good control policies for various complex tasks, where embodied AI simulators play an essential role to facilitate relevant researchers. However, existing open-sourced simulators for surgical robot are still not sufficiently supporting human interactions through physical input devices, which further limits effective investigations on how human demonstrations would affect policy learning. In this paper, we study human-in-the-loop embodied intelligence with a new interactive simulation platform for surgical robot learning. Specifically, we establish our platform based on our previously released SurRoL simulator with several new features co-developed to allow high-quality human interaction via an input device. With these, we further propose to collect human demonstrations and imitate the action patterns to achieve more effective policy learning. We showcase the improvement of our simulation environment with the designed new features and tasks, and validate state-of-the-art reinforcement learning algorithms using the interactive environment. Promising results are obtained, with which we hope to pave the way for future research on surgical embodied intelligence. Our platform is released and will be continuously updated in the website: https://med-air.github.io/SurRoL/

本文研究人形态智能的应用于外科机器人学习的新交互式模拟平台，以人机交互的形式，以增加更有效的策略学习，通过收集人类示范并模仿动作模式，展示了仿真环境的改进和验证最先进的强化学习算法。

具备交互式仿真环境的人与机器人协同的手术机器人学习