Michael Yip, Ph.D.

Assistant Professor
Electrical & Computer Engineering
Atkinson Hall 6121
9500 Gilman Drive MC0436
La Jolla, CA 92093-0436

Tel: (858) 822-4778
Email: yip@ucsd.edu

Faculty, Contextual Robotics Institute

Machine Learning / Data Sciences Curriculum Advising Hours
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.

Statement on Research Interests

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.


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

ECE276C Robot Reinforcement Learning
ECE285 Advanced Robot Manipulation
ECE115 Fast Prototyping

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
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