Affiliations
Assistant Professor, Electrical and Computer Engineering
Affiliate Professor, Mechanical and Aerospace Engineering Faculty, Contextual Robotics Institute Faculty, Halıcıoğlu Data Science Institute
Affiliate Professor, Artificial Intelligence Group
Director, Advanced Robotics and Controls Laboratory (ARCLab)Thurs. 9:00am - 10:00am (no office hours on 1/31, 3/21) Short Bio
Michael Yip is an Assistant Professor of Electrical and Computer Engineering at UC San Diego, IEEE RAS Distinguished Lecturer, Hellman Fellow, and Director of the Advanced Robotics and Controls Laboratory (ARCLab). His group currently focuses on solving problems in data-efficient and computationally efficient robot control and motion planning through the use of various forms of learning representations, including deep learning and reinforcement learning strategies. His lab applies these ideas to surgical robotics and the automation of surgical procedures. Previously, Dr. Yip's research has investigated different facets of model-free control, planning, haptics, soft robotics and computer vision strategies, all towards achieving automated surgery. Dr. Yip's work has been recognized through several best paper awards at ICRA, including the 2016 best paper award for IEEE Robotics and Automation Letters. Dr. Yip has previously been a research associate with Disney Research in Los Angeles involved in animatronics design, and most recently held a visiting research position with Amazon Robotics Machine Learning and Computer Vision group in Seattle. He received a B.Sc. in Mechatronics Engineering from the University of Waterloo, an M.S. in Electrical Engineering from the University of British Columbia, and a Ph.D. in Bioengineering from Stanford University. Robots currently lack a strong set of algorithmic tools to deal with uncertainty and dynamic environments, whether it be in the home, in a semi-automated warehouse, or in a robotic surgical operating room. Unlike the past decade of robot applications that primarily focused on highly repetitive assembly line tasks, the robots of the future will need to interact with new and changing environments. My research interests are in learning-based representations for robots that enable robots to explore and adapt control to new environments and conditions, enabling responsive artificial intelligence, planning, and execution in dynamic environments. These representations are trained using a variety of local and global model-free learning strategies, and when implemented are comparatively significantly faster, more consistent, and more power and memory efficient than state-of-art robots. The problems I am interested in solving are in the areas of robot manipulation, soft robots, and robotic surgery. Furthermore, as we consider new complex tasks for robots to perform, such as in automating robotic surgery, we come across the need to develop new robotic systems to reach those goals. Thus, a parallel research interest is to develop new, dextrous robot manipulators for surgery that include snake-like robots and MRI/CT/Ultrasound-safe robotic platforms. Publications
Areas of Research Reinforcement Learning for Robots Computationally Efficient Robot Planning Robot Manipulation in Dynamic Environments Autonomous Robotic Surgery Mechanical Design of Surgical Robots Continuum and Snake-like Robots Artificial Muscles Teaching ECE276C Robot Reinforcement Learning ECE285 Advanced Robot Manipulation ECE115 Fast PrototypingPh.D. in Bioengineering, Stanford University
M.Sc. in Electrical and Computer Engineering, University of British Columbia B.Sc., in Mechatronics Engineering, University of Waterloo Previous Positions
Amazon Robotics, Machine Learning and Computer Vision Research Group, 2018 Disney Research, Imagineer (Research Associate), 2014 Harvard University, Research Assistant, 2008 Massachusetts Institute of Technology, Research Assistant, 2007 Massachusetts General Hospital, Research Assistant, 2006 Select Awards Select Awards
IEEE Robotics and Automation Society Distinguished Lecturer, 2018 Hellman Fellow, 2017 Outstanding Researcher Award, NIH Center for Simulation in Rehab. Research 2017 Best Paper Award, IEEE Robotics and Automation Letters, 2017 Best Paper Finalist, Int. Conf. on Robotics and Automation (ICRA) 2015 Best Paper Award for Advances in Flexible Robotics for Medical Interventions, (ICRA) 2014 Robotics, Science, and Systems (RSS), Primary Area Chair Associate Editor, IEEE Robotics and Automation Letters (RA-L) Associate Editor, IEEE International Conference on Robotics and Automation (ICRA) IEEE Haptics Symposium, Sponsorship Chair NSF Panel Reviewer – National Robotics Initiative 2.0 Contributing Author, US Congressional Robotics Roadmap 2016 Complete CV |
