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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. 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 inaugural best paper award for IEEE's Robotics and Automation Letters. Dr. Yip has previously been 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

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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 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

• 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, 2020

Full Address

Atkinson Hall 6121

9500 Gilman Drive MC0436

La Jolla, CA 92093-0436

Tel: (858) 822-4778

Email: yip@ucsd.edu

Complete CV

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