Michael Yu Wang

Plenary Talk

Friday, August 23, 2024

17:00-17:50, Meeting Room B

Vision-Based Tactile Sensing for Robot Manipulation

and Learning

Michael Yu Wang

Dean and Chair Professor

School of Engineering, Great Bay University, China

Abstract:

This presentation will give an overview of the trend in the development of embedded intelligence and human-centered robots.  It will also focus on our research work on developing tactile sensors for robotic hands with dexterous and versatile capability for grasping and adaptive manipulation. Embodied intelligence requires and leverages a physical body, such as a robot, to achieve autonomous and rich physical interactions with the surrounding environment, which must be enabled by environment perception with vision and tactile sensing. In recent years we have seen rapid advancements in tactile sensor development and robot learning of agile grasping and manipulation with multi-modality information. We argue that the multi-modal manipulation capabilities of human-centered robots are essential for a feasible large-scale deployment of the future robotic technology with a potential of commercial success.

Biography:

Michael Yu Wang is a Chair Professor and the Founding Dean of the School of Engineering of the Great Bay University. He has served on the engineering faculty at University of Maryland, Chinese University of Hong Kong, National University of Singapore, Hong Kong University of Science and Technology, and Monash University. He has numerous professional honors–Kayamori Best Paper Award of 2001 IEEE International Conference on Robotics and Automation, the Compliant Mechanisms Award-Theory of ASME 31^st Mechanisms and Robotics Conference in 2007, Research Excellence Award (2008) of CUHK, and ASME Design Automation Award (2013). He was the Editor-in-Chief of IEEE Trans. on Automation Science and Engineering, and served as an Associate Editor of IEEE Trans. on Robotics and Automation and ASME Journal of Manufacturing Science and Engineering. He is a Fellow of ASME, HKIE and IEEE. He received his Ph.D. degree from Carnegie Mellon University.