面向手术机器人学习的、具有交互式仿真环境的人在回路具身智能
Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learning
AI总结：
本文扩展SurRoL仿真器，构建支持输入设备交互的人在回路手术机器人学习平台，并通过人类演示与强化学习验证其学习效率提升，同时发布五个训练任务。
摘要：
在过去十年中，手术机器人自动化吸引了越来越多的研究兴趣，人们期待其有可能使外科医生、护士和患者受益。近来，具身智能的学习范式已经展现出为各种复杂任务学习良好控制策略的可喜能力，其中具身AI仿真器在推动相关研究方面发挥着关键作用。然而，现有的开源手术机器人仿真器仍不能充分支持通过物理输入设备进行的人类交互，这进一步限制了对人类演示将如何影响策略学习的有效研究。在本工作中，我们研究人在回路的具身智能，并提出一个用于手术机器人学习的新型交互式仿真平台。具体而言，我们在先前发布的SurRoL仿真器基础上构建该平台，并共同开发了若干新特性，以允许通过输入设备进行高质量的人类交互。我们展示了仿真环境凭借所设计的新特性获得的改进，并以人类演示和强化学习的使用作为代表性示例，验证了在具身智能中纳入人类因素的有效性。在学习效率方面取得了可喜结果。最后，我们开发并发布了五个新的手术机器人训练任务，希望借此为未来的手术具身智能研究铺平道路。我们的学习平台已公开发布，并将在网站 https://med-air.github.io/SurRoL 上持续更新。
英文摘要：
Surgical robot automation has attracted increasing research interest over the past decade, expecting its potential to benefit surgeons, nurses and patients. Recently, the learning paradigm of embodied intelligence has demonstrated promising ability to learn good control policies for various complex tasks, where embodied AI simulators play an essential role to facilitate relevant research. 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 the human demonstrations would affect policy learning. In this work, 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. We showcase the improvement of our simulation environment with the designed new features, and validate effectiveness of incorporating human factors in embodied intelligence through the use of human demonstrations and reinforcement learning as a representative example. Promising results are obtained in terms of learning efficiency. Lastly, five new surgical robot training tasks are developed and released, with which we hope to pave the way for future research on surgical embodied intelligence. Our learning platform is publicly released and will be continuously updated in the website: https://med-air.github.io/SurRoL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
