Jumpei Arata is currently an Associate Professor in Department of Mechanical Engineering, Faculty of Engineering, Kyushu University, and a Guest Associate Professor in Department of Mechanical Engineering, Faculty of Engineering, the University of Tokyo, and was a Guest Professor in ETH Zurich in a short period (3 months) in 2010. He received M. Eng. degree in Shibaura Institute of Technology and Ph. D. degree in the University of Tokyo. During his studies in Master course and Ph.D, he had been an exchange student and a Swiss Federal scholarship student in the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, in 1997-1998 and 1999-2000 respectively. He has been working on Medical Robotics, specifically, Surgical Robot since his study in M. Eng. Through his Ph.D thesis, he build a tele-surgical robotic system and conducted several times of in-vivo tele-surgery experiments between Japan-Korea and Japan-Thailand. He is currently working on a surgical robot using flexible structure, that can miniaturize robotic forceps with 4 degree-of-freedom at the tip to the diameter of 2 mm. His research interests include Surgical Robot, Rehabilitation Robot, Parallel Mechanism, Soft Robotics, Compliant Mechanism and Haptics. His research work introduction, publications can be found in: http://system.mech.kyushu-u.ac.jp/member-arata.html
Talk # 1
Synthetic Elastic Mechanism - Neurosurgical Robotic Forceps within 2 mm in diameter
Clinical demands for minimal invasiveness in surgery is increasing, as it is beneficial for both patients and medical economy. Surgical robots have been widely studied for their ability to perform minimally invasive surgery. The miniaturized robotic tools play a key role in reducing the size of the wound while providing multiple degrees-of-freedom (DOF) inside the body cavity for performing dexterous motions required for the surgery. Many studies have been conducted on miniaturized surgical robotic tools, and most of the mechanisms have been developed based on wire, link, and pneumatic mechanisms. Here, we propose a surgical robotic tool using a synthetic elastic beam structure that is largely deformed during motion to transmit and transform the motion at the tip of the miniaturized surgical tool. The elastic mechanism enables further compaction and simplicity, as an elastic structure requires a lesser number of mechanical parts than that required in the other mechanisms. Recently, we succeeded to implement a prototype within the diameter of 2mm and 4 DOF at the tip. At the best of our knowledge, the developed prototype is the world smallest robotic forceps with 4 DOF at the tip. We implemented the robotic forceps for transnasal endoscopic pituitary tumor resection that requires a fine manipulation in a deep and confined area.
Talk # 2
What is “intuitiveness” in surgical robot? An attempt to increase the usability by inducing illusions
Intuitiveness in robotic surgery is highly desirable when performing highly elaborate surgical tasks using surgical master–slave systems (MSSs), such as suturing. To increase the operability of such systems, the time delay of the system response, haptic feedback, and eye–hand coordination are the issues that have received the most attention. In addition to these approaches, we propose a surgical robotic system that induces a multisensory illusion. In our previous study, we reported that a robotic instrument we devised enhances the multisensory illusion in a traditional rubber hand illusion. By having inspired this finding, we introduce the same concept to a multi-degree-of-freedom surgical robotic system for a further intuitive control. One of the biggest challenges is that the appearance and response time of the system are found to be key factors for improved intuitiveness. We thus developed a surgical robotic system with a wearable master and a compact slave system. Further, we recently developed a miniaturized hand that has 15 degree-of-freedom with 10 mm in diameter, and the integration is currently on-going. We hope to present the results soon in a conference.
Toronto (ON), Canada
Prof. Jessica Burgner-Kahrs is founder and director of the Laboratory for Continuum Robotics at Leibniz Universität Hannover, Germany since November 2015. She graduated from Universität Karlsruhe
(TH), Germany in computer science and awarded a doctoral degree at Karlsruhe Institute of Technology (KIT), Germany. Before she started at Leibniz Universität Hannover in 2013, she was Research Associate at Vanderbilt University, USA for two years. Her research focus lies on small-scale continuum robotics, particularly their design, modeling, planning and control as well as human-robot interaction. Applications range from minimally invasive surgery to maintenance, repair, and operations of capital goods. In 2015, she was awarded with the Heinz Maier-Leibnitz Prize, the Lower Saxony Science Award in the category Young Researcher and entitled Young Researcher of the Year 2015 in Germany. The Berlin-Brandenburg Academy of Sciences awarded her the Engineering Science Prize in 2016. She was elected among the Top 40 under 40 in the category Science and Society in 2015, 2016 and 2017 by the business magazine Capital.
Talk # 1
Continuum Robots - Developments and Challenges on a Millimeter Scale
Continuum robots are not composed of discrete joints or rigid links and thus differ substantially from conventional robots. Their structure is inspired by nature, in particular by the animal kingdom, e.g. elephant trunks, anteater tongues, or tentacles. Continuum robots are composed of flexible, elastic, or soft materials such that they can exhibit complex bending motions and achieve dexterous manipulation even in constrained environments. The high scalability and miniaturization potential allow for numerous applications, e.g. minimally invasive surgery through natural orifices or inspection of capital goods such as aircraft engines. The presentation gives an overview on continuum robot designs and touch upon fundamentals in kinematic modeling, planning and control. The merits of continuum robots are discussed for example applications and open research questions and challenges are elaborated.
Talk # 2
Computational Challenges and Applications of Continuum Robotics
Continuum robots are composed of flexible, elastic, or soft materials such that they can exhibit complex bending motions and achieve dexterous manipulation even in constrained environments. The high miniaturization potential allows for numerous applications, e.g. minimally invasive surgery through natural orifices or inspection of capital goods such as aircraft engines. Computational challenges are associated with the highly nonlinear kinematics, workspace characterization, as well as planning and control. After a general introduction to continuum robotics, the talk focusses on kinematic modeling using methods from differential geometry and elasticity theory and on elevating these concepts to planning problems. The talk concludes with future challenges and applications.
San Diego (CA), USA
Michael Yip is an Assistant Professor and the Director of the Advanced Robotics and Controls Lab at UCSD. His research focuses on three areas: (1) flexible robots for surgery, (2) visual computation for image-guided robots, and (3) robotic actuators for bionic devices. Recent efforts in his research group involve building, controlling, and automating endoscopic and catheter robots for treating heart and lung disease, designing artificial intelligence for robot-human collaboration in surgery, and augmenting surgeon teams with augmented reality for minimally invasive surgery. His work in flexible surgical robots and robot muscles have been nominated and have won best paper awards in major robotics conferences. Dr. Yip has been a visiting researcher at the Harvard University and MIT in the area of surgical robotics and tissue engineering, and a research associate with Walt Disney Research in Los Angeles working on next-generation animatronics. He received a Bachelors in Mechatronics Engineering from the University of Waterloo, a Masters in Electrical Engineering from the University of British Columbia, and a Ph.D. in Bioengineering from Stanford University.
Talk # 1
Learning to control and plan surgical robots within the body
Surgical robotics offers an unprecedented ability to place and dexterously control small robotic instruments, immersive stereo imaging and other sensing modalities deep within inaccessible locations in the body. This presents major opportunities to in the medical domain to treat diseases (e.g. cardiac arrhythmia, lung cancer, colon cancer) in a minimally invasive fashion beyond. Yet, as these devices get smaller, more flexible and more mechanically complex, we are presented with a new challenge: do we rely on the doctor to sort out the challenging control of the devices while simultaneously processing the multi-modal biosignals from onboard sensing? Or do we off-load the low-level control of the surgery from human teleoperation onto a semi-autonomous or fully-autonomous framework? I will discuss our work in developing robot-assisted surgeries that analyze a multimodal spectrum of sensory information, physics models, and imaging information in real-time to optimally plan and perform semi-autonomous surgery. This includes real-time learning-based controllers for automating catheter and endoscopic robots within difficult anatomy, modular snake-like devices for efficient locomotion in difficult environments, visual computation methods for image-guided robotics, and robot intelligence for robot-human teams. Finally, I will discuss directions we aim to pursue in reinforcement learning such that with limited self-training, our robot-assistive devices learn to become expert robot surgeons.
Talk # 2
Learning Model-free Representations for Fast, Adaptive Robot Control and Motion Planning
Robot manipulation has traditionally been a problem of solving model-based control and motion planning in structured environments. This has made them very well suited for a finite set of repeating tasks and trajectories such on a manufacturing assembly line. However, when considering more complex and partially-observable environments, and when more complex and compliant and safe robots are proposed, outcomes of robot actions become more and more uncertain, and model-based methods tend to fail or produce unexpected results. Erratic behavior makes robots dangerous in human environments and thus new approaches must be taken. In this talk, I will discuss our research in learning model-free representations for robots that enable robots to learn and adapt their control to new environments and conditions, and perform fast motion planning and adaptation to changing environments. These representations are trained using a variety of local and global model-free learning strategies, and when implemented are comparatively significantly faster, more consistent, and more power and memory efficient than conventional control and trajectory planners. I will relate its relevance to surgical robotics and a new frontier in autonomous surgery.