会议程序

嘉宾简介：刘宏斌，中科院香港创新研究院人工智能与机器人创新中心执行副主任，中国科学院自动化研究所研究员，博导，中科院自动化所智能微创医疗技术团队负责人。入选国家高层次人才计划，中科院海外高层次人才计划，某部委重点人才基金。刘宏斌研究员在英国伦敦国王学院学习工作15年，归国前任医疗机器人学教授,触觉医疗机器人实验室主任。2020年全职加入中国科学院自动化所。为解决触感能力缺失这一当前制约微创手术效率及安全性的关键性难题，以实现手术微创化，数字化，智能化，标准化为目标，长期致力于研发经自然腔道柔性微创机器人技术。所带领的实验室是国际上率先开展具有触觉感知的柔性手术机器人研发的团队之一。刘宏斌研究员在精准触觉传感技术、微创柔性机器人技术，多模态信息融合与控制相关的理论研究等方面取得了业内公认的开创性成果。所研发具有多模态融合感知的微创颅内柔性机器人MicroNeuro系统,微创手术器械复杂微曲面分布式触觉感知ESSENC技术、在理论与技术实践上处于世界领先地位。共发表国际机器人顶尖期刊与会议超过120余篇，H影响因子37。发明专利申请、授权40余项。

报告题目：advanced machine imaging for robotic endoscopic surgery

报告摘要：In this talk I will review the technology advancement in robotic endoscopic surgery and their applications and share our research on surgical instrument segmentation, depth estimation using with monocular endoscopic camera, as well as flexible instrument localization based on endoscopic imaging.

嘉宾简介：Dr. Henry Kar Hang Chu received his B.A.Sc. degree in Mechanical Engineering from the University of Waterloo, Waterloo, Canada, his M.A.Sc. and Ph.D. degrees in Mechanical and Industrial Engineering from the University of Toronto, Toronto, Canada.  In 2015, he joined the Department of Mechanical Engineering, the Hong Kong Polytechnic University, Hong Kong, China as an Associate Professor. His research interests include microscale robot control and automation, visual servoing, continuum robot design, dielectrophoresis for tissue engineering applications. 

报告题目：Image-based control of soft continuum robot for fluid suction

报告摘要：Soft continuum robot has been an emerging research topic, opening new possibilities to conceptualize, design and fabricate cost-efficient robots for various applications. Owing to their advantages of high flexibility with infinite degrees of freedom (DOF), one of the potential applications is to perform blood suction. In this talk, I will first introduce the continuum robot design and the mechanism to attain higher efficiency in suctioning. Next, I will cover the image-based algorithms to achieve precise robot control and robot shape reconstruction. Finally, I will present our latest research on a simulation platform that can visualize the current pose of a multi-segment continuum robot to aid the robot planning and control in real time. 

嘉宾简介：Hongliang Ren received his Ph.D. in Electronic Engineering (Specialized in Biomedical Engineering) from The Chinese University of Hong Kong (CUHK) in 2008. He serves as an Associate Editor for IEEE Transactions on Automation Science & Engineering (T-ASE) and Medical & Biological Engineering & Computing (MBEC).  He has navigated his academic journey through Chinese University of Hong Kong, Johns Hopkins University, Children’s Hospital Boston, Harvard Medical School, Children’s National Medical Center, United States, and National University of Singapore (NUS). He is currently Associate Professor, Department of Electronic Engineering at Chinese University of Hong Kong, and Adjunct Associate Professor, Department of Biomedical Engineering at National University of Singapore. His areas of interest include biorobotics, intelligent control, medical mechatronics, soft continuum robots, soft sensors, and multisensory learning in medical robotics. He is the recipient of NUS Young Investigator Award and Engineering Young Researcher Award, IAMBE Early Career Award 2018, Interstellar Early Career Investigator Award 2018, ICBHI Young Investigator Award 2019, and Health Longevity Catalyst Award 2022 by NAM & RGC.

报告题目：Surgical motion generation and motion understanding towards augmented minimally invasive robotic procedures

报告摘要：This talk will highlight some recent developments in dexterous robotic motion generation with motion understanding towards image-guided minimally invasive procedures. The procedure-specific telerobotic surgical systems can assist surgeons in performing dexterous manipulations using the master-slave console bilateral motion generation & mapping mechanism with variable stiffness.Meanwhile, surgical motion understanding aims to learn from the multi-domain surgical perceptions and describe the semantic relationship between instruments and surgical region of interest. Automatically understanding the instrument motions in robotic surgery is crucial to enhance surgical outcomes, enable surgical camera automation, and facilitate surgical training. To that end, we generate the task-aware saliency maps and scanpath of the instruments beyond tracking and segmentation, similar to the surgeon’s visual perception, to get the priority focus on selected surgical instruments. Furthermore, generating a surgical report in robot-assisted surgery, together with surgical scene understanding, can play a significant role in document entry tasks, surgical training, and post-operative analysis.