Natsuki Yamanobe is a senior researcher of Industrial Cyber-Physical Systems Research Center at the National Institute of Advanced Industrial Science and Technology (AIST) and a guest associate professor at Tokyo University of Agriculture and Technology. She received her M.E. and Ph.D. degrees from the University of Tokyo in 2004 and 2007, respectively. In 2007 she started with AIST. For one year from 2014 to 2015 she was a visiting researcher at Karlsruhe Institute of Technology. Her research interests include robotic manipulation, human-robot interaction, skill analysis/transfer for dexterous manipulation.
Recently, co-worker scenarios where robots and humans work together sharing a workspace, became a topic of great interest also in manufacturing fields. What are the requirements of co-worker robots? In addition to the efficiency, robots should behave so as to be perceived safe and comfortable by the humans working together with them to ensure their acceptance. In this talk, I would like to present several works conducted to know what aspects of robot’s behavior affect human’s feelings from the viewpoint of common sense, semantics, and more automatic emotions like fear and anxiety. The framework of a cyber-physical system for human-robot collaboration is also introduced. The whole situation including environment, humans, and robots is continuously recognized and expressed in a cyber space, where the next possible situations are simulated, and orders for the robots are planned for a comfortable collaboration.
Professor Dongheui Lee is Associate Professor of Human-centered Assistive Robotics at the TUM Department of Electrical and Computer Engineering. She is also director of a Human-centered assistive robotics group at the German Aerospace Center (DLR). Her research interests include human motion understanding, human robot interaction, machine learning in robotics, and assistive robotics. Prior to her appointment as Associate Professor, she was an Assistant Professor at TUM (2009-2017), Project Assistant Professor at the University of Tokyo (2007-2009), and a research scientist at the Korea Institute of Science and Technology (KIST) (2001-2004). After completing her B.S. (2001) and M.S. (2003) degrees in mechanical engineering at Kyung Hee University, Korea, she went on to obtain a PhD degree from the department of Mechano-Informatics, University of Tokyo, Japan in 2007. She was awarded a Carl von Linde Fellowship at the TUM Institute for Advanced Study (2011) and a Helmholtz professorship prize (2015). She is coordinator of both the euRobotics Topic Group on physical Human Robot Interaction and of the TUM Center of Competence Robotics, Autonomy and Interaction.
As a fundamental cornerstone in the development of intelligent robotic assistants, the research community on robot learning has addressed autonomous motor skill learning and control in complex task scenarios. Imitation learning provides an efficient way to learn new skills through human guidance, which can reduce time and cost to program the robot. Robot learning architectures can provide a comprehensive framework for learning, recognition and reproduction of whole body motions. The inference mechanism can be applied not only to learn the robot's free body motion but also to learn physical interaction tasks, including human robot interaction. I will give examples of cognition enabled assistive robotics, including enhancement of human-robot cooperation tasks over time and intuitive programming co-bots in industrial setting.
From 2019, Dr. Xiaolong Feng works as a Business Research Manager at ABB Robotics and Discrete Automation Business, responsible for a global team with focus on long-term robotics research. Between 2015 and 2018, he served as a Global Research Area Manager at ABB Corporate Research, responsible for Mechatronics research in industrial and service robotics and for long-term and fundamental research in Mechanics for all Divisions at ABB. In 2014, he was promoted as a Corporate Research Fellow at ABB Corporate Research in Optimal Mechanical Design. Between 2019 and 2014, he worked as a Senior Principal Scientist in Mechatronic Design at ABB Corporate Research Center in Sweden. He worked with modeling, simulation, design, optimization of industrial robotic manipulators between 2000 and 2014. In addition, he worked as a project manager between 2002 and 2014 in a number of large research projects in the area of efficient and optimal design of industrial robot manipulators. His academia merits include: He received Ph.D. at Stockholm University in 1998 in the research area of modeling and simulation of Quantum Mechanical systems. He was awarded Docent in Machine Design at Linköping University in 2012. He worked also as an adjunct professor at Linköping University between 2012 and 2019. He has about 50 scientific publications in journals and proceedings.
The increasing demand of flexibility of robotic automation in discrete manufacturing industry and the increasing need in robotic assistance solutions in healthcare, professional elderly homes, restaurants and in domestic environment require increasing level of autonomy of future robots – future autonomous robots. A future autonomous robot is intelligent, mobile, connected and safe that can work together with humans in dynamic and unstructured environment. Future autonomous robots will be enabled by four cornerstone technologies: Intelligence, Safety for collaboration, Mobility and Connectivity. In this presentation, the identified four cornerstone technologies will be discussed. A number of major trends in technology development that would become key enablers for these cornerstones will be summarized. This presentation will focus specifically on the presentation of research progress in two of the cornerstone technology areas, namely intelligence and mobility. The presented research progresses are achieved either by ABB global research teams or by our research partners in academia. In the intelligence research area, the progress of our research activities on several levels of intelligence will be presented: from introducing intelligence of the finger tip of a robotic gripper, AI based perception, autonomous grasping, to high level end to end learning methodology. In the mobility research area, the research progress of the following topics will be addressed: Multimodal sensing and advanced navigation, semantic SLAM, and ROS drive for motion planning and control of a mobile manipulator, a Mobile YuMi robot, developed by ABB.
Dr. Masaaki Wada is a professor of Future University Hakodate, Japan. He received Ph.D. in the field of Marine Environment and Resources at the Graduate School of Fisheries Sciences, Hokkaido University. His main research interest is in smart fisheries based on information and communication technology. From 1993 to 2004, he was an engineer in Towa Denki Seisakusho Co., Ltd., Hakodate, Japan. Since 2005, he has been in Future University Hakodate. He is a member of the IEEE and IPSJ. He received Minister of Internal Affairs and Communications Award in 2016.
The global fisheries production exceeded 200 million ton in 2017. In particular, aquaculture production has been increasing year by year, and overtook capture production in 2013. Contrary to the global trends, capture production accounts for three-quarters of fisheries production in Japan. In the case of capture, we cannot achieve the sustainable fisheries without resource management. However, the advanced technologies of fishery equipment such as acoustic sonar has led to increase the fishing pressures and decrease the resources. In 2019, Japan Fisheries Agency launched a project of smart fisheries toward the sustainable fisheries. In this lecture, as a local topic in Hokkaido, I will introduce some practices of utilizing these advanced technologies not for over catch but for appropriate catch.