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/
翻译:过去十年来,外科机器人自动化已经吸引了越来越多的研究兴趣,期望其巨大的潜力有利于外科医生、护士和病人。最近,体现的AI的学习范式展示了学习各种复杂任务的良好控制政策的良好能力,其中体现的AI模拟器对于促进相关研究人员具有重要作用。然而,现有的外科机器人开源模拟器仍然不能通过物理输入装置充分支持人类互动,这进一步限制了对人类演示如何影响政策学习的有效调查。在本文中,我们研究人类在手术机器人学习的新型互动模拟平台中体现的智能。具体地说,我们以我们以前发行的SurRoL模拟器为基础,建立了我们的平台,其一些新功能是共同开发的,以便允许高质量的人类互动,通过输入装置。我们进一步提议收集人类演示品,并模仿行动模式,以便更有效地进行政策学习。我们用设计的新特征和任务展示我们的模拟环境,并用互动环境验证最新技术强化的学习算法。我们获得了预测结果,我们希望通过这个平台为未来研究铺路。