The Computational Intelligence and Robotics Lab (CIR Lab)
Research Field
Chen-Chien James Hsu was born in Hsinchu, Taiwan. He received the B.S. degree in electronic engineering from the National Taiwan University of Science and Technology, Taipei, Taiwan, in 1987, the M.S. degree in control engineering from National Chiao-Tung University, Hsinchu, in 1989, and the Ph.D. degree from the School of Microelectronic Engineering, Griffith University, Brisbane, QLD, Australia, in 1997. He was a Systems Engineer with IBM Corporation, Taipei, for three years, where he was responsible for information systems planning and application development, before commencing his Ph.D. studies. He joined the Department of Electronic Engineering, St. John’s University, Taipei, as an Assistant Professor, in 1997, and was appointed as an Associate Professor in 2004. From 2006 to 2009, he was with the Department of Electrical Engineering, Tamkang University, Taipei. He is currently a Distinguished Professor with the Department of Electrical Engineering, National Taiwan Normal University, Taipei. He is the author or co-author of more than 250 refereed journal and conference papers. His current research interests include Learning from Demonstration (LfD) Robotic Systems, Human Action Recognition, Mobile Robot Navigation, Digital Control Systems, and Evolutionary Computation. He is an IET fellow.
The Computational Intelligence and Robotics (CIR) Lab was established in 2007. It is jointly supervised by Prof. Wei-Yen Wang, IEEE Fellow, and Prof. Chen-Chien Hsu, IET Fellow. Research areas of the CIR Lab include intelligent systems, neural networks, fuzzy systems, evolutionary computation, mobile robot navigation, Human Action Recognition, and Learning from Demonstration (LfD) Robotic Systems.
There are currently 16 members in the CIR Lab, including 1 postdoc researcher, 1 research assistant, 2 PhD students, and 12 Master students. Over the past years, the CIR Lab has had excellent academic performances in terms of paper publications, patents, industry-academy cooperation projects, government-sponsored projects, as well as technological competitions.
Over the past three years (2019/01-2021/12), a total of 8 projects with research funding up to 37,000,000 NTD have been awarded to Prof. Wang and Prof. Hsu to conduct robotic projects. Currently, the CIR Lab is focusing on the design and implementation of Learning from Demonstration (LfD) Robotic Systems, Smart Video Surveillance Systems, Mobile Collaborative Robotic Systems, and crowd-aware robot navigation systems, which are jointly supported by the National Taiwan Normal University (NTNU) within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE), Taiwan, and National Science and Technology Council (NSTC), Taiwan.
Over the past three years (2019/01-2021/12), detailed academic achievements of the CIR Lab over the last three years are listed below:
● Research projects supported by NSTC: 8
● Industry-academy cooperation projects: 6
● Total research funding: 37,000,000 NTD
● Patent granted: 3
● Awards received by the CIR members: 9
● Journal papers published: 13
● International conference papers published: 27
● Domestic conference papers published: 10
Project 1: Multi-objective crowd-aware robot navigation system using deep reinforcement learning
Project 2: Collaborative Robots with Active Safety Protection Mechanism
Project 3: Deep Learning-Based Intelligent Video Surveillance System
Prof. Hsu has published more than 250 refereed journal and conference papers. His current research interests include Learning from Demonstration (LfD) Robotic Systems, Human Action Recognition, Mobile Robot Navigation, Digital Control Systems, and Evolutionary Computation. He is a distinguished professor with the Department of Electrical Engineering, National Taiwan Normal University, and an IET fellow.
Prof. Hsu received the B.S. degree in electronic engineering from the National Taiwan University of Science and Technology, Taipei, Taiwan, in 1987, the M.S. degree in control engineering from National Chiao-Tung University, Hsinchu, in 1989, and the Ph.D. degree from the School of Microelectronic Engineering, Griffith University, Brisbane, QLD, Australia, in 1997.
1 Vacancy
Job Description
Students are expected to work with fellow students here at CIR Lab under the guidance of the mentor. The expectation is for students to generate substantial research outcomes and compile a report by the conclusion of the internship. Those with outstanding research capabilities can receive financial support to pursue their postgraduate studies at NTNU after the internship.
Preferred Intern Education Level
Bachelor holders and postgraduate students
Skill sets or Qualities
Proficiency in programming languages, such as Python or C++, and a solid understanding of fundamental concepts in Deep Learning are required. Prior experience in deep reinforcement learning or robot navigation is highly desirable.
1 Vacancy
Job Description
Students are expected to work with fellow students here at CIR Lab under the guidance of the mentor. The expectation is for students to generate substantial research outcomes and compile a report by the conclusion of the internship. Those with outstanding research capabilities can receive financial support to pursue their postgraduate studies at NTNU after the internship.
Preferred Intern Education Level
Bachelor holders and postgraduate students
Skill sets or Qualities
Proficiency in programming languages, such as Python or C++, and a solid understanding of fundamental concepts in Deep Learning are required. Prior experience in robot control is highly desirable.
1 Vacancy
Job Description
Students are expected to work with fellow students here at CIR Lab under the guidance of the mentor. The expectation is for students to generate substantial research outcomes and compile a report by the conclusion of the internship. Those with outstanding research capabilities can receive financial support to pursue their postgraduate studies at NTNU after the internship.
Preferred Intern Education Level
Bachelor holders and postgraduate students
Skill sets or Qualities
Proficiency in programming languages, such as Python, and a solid understanding of fundamental concepts in Deep Learning are required. Prior experience in action or object detection is highly desirable.