Robot Perception and Learning Lab.


Lab Name and Affiliation

Robot Perception and Learning Lab.

Department of Computer Science and Information Engineering, Graduate Institute of Networking and Multimedia, National Taiwan University

Lab Director (or Principal Investigator)

Chieh-Chih (Bob) Wang

Chieh-Chih (Bob) Wang earned his Ph.D. in Robotics from the School of Computer Science at Carnegie Mellon University in 2004. He received his B.S. and M.S. in Engineering Science and Ocean Engineering from National Taiwan University in 1994 and 1996, respectively. During his graduate study, he worked with the Bayesian vision group at the NASA Ames research center and at Z+F Inc. in Pittsburgh. From 2004 to 2005, he was an Australian Research Council (ARC) Research Fellow of the ARC Centre of Excellence for Autonomous Systems and the Australian Centre for Field Robotics at the University of Sydney. In 2005, he joined the Department of Computer Science and Information Engineering at National Taiwan University, where he is an associate professor and is pursuing his academic interests in robotics, machine perception and machine learning. Dr. Wang received the best conference paper awards at the 2003 IEEE International Conference on Robotics and Automation (ICRA) and at the 2010 Conference on Technologies and Applications of Artificial Intelligence (TAAI) and the best reviewer award at the 2007 Asian Conference on Computer Vision (ACCV).

Lab Introduction

Our scientific interests are driven by the desire to build intelligent robots, computers and embedded systems, which are capable of servicing people more efficiently than equivalent manned systems in a wide variety of dynamic and unstructured environments. We are primarily driven by the problems of how robots learn about their environment under uncertainty and with incomplete information. As computers and sensors become ubiquitous, the importance and need of scene understanding will increase substantially in the coming decades. Theoretical frameworks as well as computationally efficient algorithms have to be established and developed at the intersection of machine perception, machine learning, control, statistics and optimization. We believe that finding a solution to scene understanding is a key prerequisite for making robotic systems truly autonomous and making many potential applications feasible.

Lab Contact E-mail