Invited Speaker 1: Assoc. Professor Tan Chee Pin
Title: State estimation and its application in intelligent robotics
Abstract:
Intelligent robotics requires significant amounts of information which are obtained through sensors. These information could be used to glean hidden knowledge of a system, diagnose the condition of a system, or to make decisions on the next course of action. However, it is not always straightforward to obtain sensor information. Sensors could be expensive, or inaccurate, infeasible to be installed, or might not even be able to measure a desired variable. An attractive solution to address this issue is state estimation, which processes available sensor information based on a mathematical model of the system to produce an estimate of variables in the system. However, in state estimation, there are challenges such as inaccuracies and noise in the sensor information as well as the model. In this talk, we will introduce the concept of state estimation, as well as some of the latest developments in the field that can accurately estimate states despite the presence of the inaccuracies and noises. We will demonstrate the potential of state estimation in a soft robotic system, where we successfully achieved proprioception and exteroception (estimate internal and external variables) using convenient and inexpensive sensors.
Biography:
Tan Chee Pin received the B.Eng. degree (Hons.) and the Ph.D. degree from Leicester University, Leicester, U.K., in 1998 and 2002, respectively. He is now an Associate Professor at the School of Engineering, Monash University Malaysia. His research interests are in the theoretical development of robust state estimation and fault diagnosis schemes, and in applying them to areas such as soft robotics, mechanical ventilation, and smart cities. He has authored more than 100 internationally peer-reviewed research articles, including a book on fault reconstruction. He serves as a member of the IEEE Control Systems Society Conference Editorial Board, and also as Associate Editor of the Journal of Franklin Institute, and IET Collaborative Intelligent Manufacturing. In recognition of his research achievements, he has also been invited to give keynote and plenary talks at several international conferences – both in the academic and industrial communities.
Invited Speaker 2: Assoc. Professor Jeffrey Too Chuan TAN
Title: Learning AI (Artificial Intelligence) by Building Service Robots
Abstract:
Learning AI (Artificial Intelligence) for beginners can be quite intimidated especially when one lacks of any computer science background. However, AI is flourishing various industries outside of its own technical domain, making AI literacy soon to be a requirement in many workplaces. The situation becomes more embarrassing when current AI education is mainly focusing on generating AI experts for the theoretical development or complex applications of AI, rather than making AI can be learned by young schoolchildren.
The RoboCup@Home Education initiative was started with a simple intention to facilitate technically complex service robot development by novice teams and even schoolchildren. The service robot development involves many practical AI applications solving real-world problems in various domains including human-robot interaction, navigation and mapping in dynamic environments, computer vision, object recognition and manipulation, and robot intelligence. Under the education initiative, a novel AI learning approach was formulated after several cycles of the organization of workshop based service robot challenge with team-centered competition design. Not only a learning framework was created, the workshop contents had generated teaching materials, and the competition design had provided yardstick for learning assessment.
The AI learning approach by service robot development is currently being further developed into a more formal education system, together with compatible hardware, software and courseware, with the aim to provide practical basic AI education for non-technical beginners and young schoolchildren.
Biography:
Jeffrey Too Chuan TAN received Bachelor of Mechanical Engineering (Hons.) and Master of Mechanical Engineering from Universiti Tenaga Nasional, Malaysia in 2003 and 2007, respectively. He received Doctor of Engineering in Precision Engineering from The University of Tokyo, Japan in 2010. He was a Project Researcher at National Institute of Informatics, Japan from 2011, developing a new simulator for human-robot interaction. From 2014, he worked as a Project Assistant Professor at Institute of Industrial Science, The University of Tokyo, Japan in advanced mobility development. In 2017, he was invited under the Tianjin City Thousand Talents Program (Young Professionals) and worked as an Associate Professor at Nankai University, China. From the same time, he is also a Visiting Researcher at Tamagawa University, Japan, to foster international collaboration. His areas of research including service robotics and human-robot interaction, cloud robotics, and advanced mobility.
He was a Principle Investigator of over 20 research projects funded by National Natural Science Foundation of China (NSFC), Japan Society for the Promotion of Science (JSPS), IEEE and RoboCup Federation. He has published over 100 research papers in technical journals and academic conferences. He was a recipient of FANUC FA Robot Foundation Best Paper Award (2011), MAZAK Foundation Advanced Manufacturing Systems Best Paper Award (2011), and Japanese Society for Artificial Intelligence (JSAI) Award (2013, 2014). He was also a winner of RoboCup @Home leagues (Japan Open 2013-2019, China Open 2018-2019, RoboCup Asia-Pacific 2017, RoboCup Asia-Pacific Tianjin 2019, and international RoboCup 2019). In 2015, he founded the RoboCup @Home Education initiative to promote learning of practical AI and robotics development. Under the initiative outreach programs, he has conducted over 40 introductory talks & demos and over 20 hands-on workshops globally to promote a new form of AI-focused Robotics Education.
Invited Speaker 3: Assoc. Professor Sian Lun Lau
Title: 30 years Ubiquitous Computing: Where do we go from here?
Abstract:
It has been an exciting 30 years journey since Mark Weiser put forward the vision of Ubiquitous Computing (Ubicomp) in 1990. He inspired many researchers to build and pursue a world where computing may appear anytime and anywhere without users being conscious about its existence. Since then, research and technology have made tremendous progress towards a world with “computers” that are increasing intuitive, smart and invisible. This talk shares the different eras and work in Ubicomp over 30 years and discuss how much have been achieved. More importantly, where will the achievements, failures and experience lead us in years to come towards the vision of Ubiquitous Robots?
Biography:
Assoc. Prof. Dr. Sian Lun Lau received his Dr.-Ing. and MSc in Electrical Communication Engineering from the University of Kassel, Germany. He also holds a BEng with Hons in Electronic and Telecommunications Engineering from Universiti Malaysia Sarawak (UNIMAS). During his nine years (2004 – 2013) as a researcher at the Chair for Communication Technology (ComTec) at the University of Kassel, he has worked and managed various German National- and EU-funded research projects. Among them are EU IST-MobiLife, ITEA S4ALL, BMBF MATRIX and EU-SEAM4US.
He joined Sunway University, Malaysia in February 2013. He is currently an Associate Professor at the Department of Computing and Information Systems and holds the responsibility as the Head of Department. Since 2015, he is also the Associate Dean for the School of Science and Technology. He continues active research and has published over 60 publications in conferences, workshops, book chapters as well as journals.
He is currently a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and serves as an executive committee member in the IEEE Computer Society Malaysia Chapter since 2017. His research interests include ubiquitous computing, sustainable smart city, context-awareness and applied machine learning.
ICIRA 2020 