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. 


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.

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Advanced Robotics and Controls Laboratory (ARCLab)

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Full Address

Atkinson Hall 6121

9500 Gilman Drive MC0436

La Jolla, CA 92093-0436

Tel: (858) 822-4778

Email: yip@ucsd.edu 

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