Polar is a Robotics Fellow in the Division of Safety Research and Center for Occupational Robotics Research at National Institute for Occupational Safety and Health (NIOSH). His research and teaching interests include Human-Robot Collaboration, Computer Vision, Reinforcement Learning, Building Information Modeling (BIM), Digital Twins, and Extended Reality (XR) to promote sustainability and safety in future construction and manufacturing sites. He received his Ph.D. in Civil Engineering and M.S. in Robotics at the University of Michigan. He also received M.S. and B.S. in Civil Engineering from National Taiwan University.


  • Robotics
  • Computer Vision
  • Reinforcement Learning
  • BIM and Digital Twins
  • Extended Reality (XR)


  • Ph.D. in Civil and Environmental Engineering, 2021

    University of Michigan

  • M.S. in Robotics, 2017

    University of Michigan

  • M.S. in Civil Engineering, 2015

    National Taiwan University

  • B.S. in Civil Engineering, 2012

    National Taiwan University


Teaching robots to perform construction tasks through demonstration

Apply Learning from Demonstration method to teach construction robots.

Vision-based pose estimation for construction robots

Develop a vision-based DNN pose estimation system.

ARCT: AR for Clinical Training

Develop an AR stroke simulation for clinical training.

RAS: Robotic Assembly System for Steel Structure

RAS, a robotic assembly system for steel structure, remove workers from high places and perform steel beam assembly tasks semi-automatically.

Sky Classroom: Global Team Project and Virtual BIM Reviewer

Global construction collaboration course taught between seven universities using BIM and online collaboration platform.

BotBeep: Warning Device for Wheelchair Rearward Safety

BotBeep, an innovative and low-cost device, was developed to warn the user of elevation changes behind the wheelchair.