Michael Yip, Ph.D.
Director, Advanced Robotics and Controls Laboratory (ARCLab)
Director, Medical Robotics Collab, Contextual Robotics Institute
Associate Professor, Electrical and Computer Engineering
Affiliate Professor, Computer Science and Engineering
Affiliate Professor, Mechanical and Aerospace Engineering
Affiliate Professor, Artificial Intelligence Group
Office: Franklin Antonio Hall Rm 3202
Tel: (858) 822-4778
Email: yip@ucsd.edu
Machine Learning / Data Sciences Curriculum Office Hours - by email only
Short Bio
Michael Yip is an Associate 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. These techniques focus on solving problems with robot manipulation and locomotion on novel, dexterous platforms, include surgical robot manipulators, continuum robots, snake-like robots, and underwater systems. 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. His work has been recognized through several best paper awards and nominations at ICRA and IROS, the 2017 best paper award for IEEE Robot and Automation Letters, as well as recognitions including the NSF CAREER award and the NIH Trailblazer award. Dr. Yip was previously a Research Associate with Disney Research Los Angeles in 2014, a Visiting Professor at Stanford University in 2019, and a Visiting Professor with Amazon Robotics' Machine Learning and Computer Vision group in Seattle, WA in 2018. 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.
Research
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 general areas of robot manipulation, constrained motion planning, 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, dexterous robot manipulators include snake-like robot platforms and MRI/CT/Ultrasound-safe robotic platforms.
Lab Website
Advanced Robotics and Controls Laboratory (ARCLab)
Publication List
Areas of Research
Robot Manipulation in Dynamic Environments
Reinforcement Learning for Robot Control
Continuum and Snake-like Robots
Robotic Surgery
visit lab website for more details.
Teaching
ECE276C Robot Manipulation and Control
ECE276C Robot Reinforcement Learning
ECE285 Advanced Robot Manipulation
ECE115 Fast Prototyping
Education
Ph.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
Stanford University, Visiting Professor in Mechanical Engineering 2019
Amazon Robotics Machine Learning and Computer Vision Group, Visiting Professor, 2018
Disney Research, Research Associate, 2014
Harvard University, Research Assistant, 2008
Massachusetts Institute of Technology, Research Assistant, 2007
Massachusetts General Hospital, Research Assistant, 2006
Select Awards
NIH Trailblazer Award, 2021
NSF CAREER Award, 2021
Best Paper Finalist, Int. Conf. on Robotics and Automation (ICRA) 2021
Elevated to IEEE Senior Member, 2021
Best Paper Award for Cognitive Robotic Surgery, Int. Conf. on Robotics and Automation (ICRA) 2020
IEEE Robotics and Automation Society Distinguished Lecturer, 2018
Hellman Fellow, 2017
Outstanding Researcher Award, NIH Center for Simulation in Rehab. Research 2017
Inaugural 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, Int. Conf. on Robotics and Automation (ICRA) 2014
Service
Senior Program Committee, IEEE International Conference on Robotics and Automation (CRA)
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 – NSF:ENG: National Robotics Initiative 2.0, NSF:IIS: Robust Intelligence, NIH: National Institute of Biomedical Engineering and Bioengineering
Contributing Author, US Congressional Robotics Roadmap 2016 & 2020
Full Address
Atkinson Hall 6121
9500 Gilman Drive MC0436
La Jolla, CA 92093-0436
Tel: (858) 822-4778
Email: yip@ucsd.edu