The Multi-Agent Robotic Systems (MARS) Lab

The Multi-Agent Robotic Systems (MARS) Lab is directed by Prof. Mo Chen in the School of Computing Science, Simon Fraser University. Our research focuses on principled robotic decision making, centred around combining traditional analytical methods in robotics and modern data-driven techniques. We address theoretical and computational challenges in robotic safety, connect control-theoretic algorithms with perception and machine learning, and bridge the gap between theory and practical implementation. Through incorporating prior knowledge and understanding of robotic systems into decision making algorithms to make robots safer and smarter, we aspire to enable more widespread use of robotic systems such as autonomous cars, unmanned aerial vehicles, and medical robots.

About Dr. Mo Chen

Mo Chen is an Assistant Professor in the School of Computing Science at Simon Fraser University, Burnaby, BC, Canada, where he directs the Multi-Agent Robotic Systems Lab. He completed his PhD in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley with Claire Tomlin in 2017, and received his BASc in Engineering Physics from the University of British Columbia in 2011. From 2017 to 2018, Mo was a postdoctoral researcher in the Aeronautics and Astronautics Department in Stanford University with Marco Pavone. His research interests include multi-agent systems, safety-critical systems, and practical robotics. Mo received the 2017 Eli Jury Award for his research and the 2016 Demetri Angelakos Memorial Achievement Award and his mentorship of students.

Research

Our research is centred around principled robotic decision making algorithms that allow complex robotic systems to perform complex tasks safely. To achieve this, we work at the intersection of traditional analytical and modern data-driven methods develop algorithms. We develop algorithms based on control theory, improve robotic learning techniques, and apply a combination of control theoretic and machine learning tools to practical problems.

  • Robotic safety
  • Reinforcement learning
  • Human intent inference
  • Visual navigation

Prospective Students

Prospective PhD students with a strong background in computer science and mathematics interested in joining the MARS Lab should email Prof. Mo Chen with resume/CV and transcript attached. More information can be found at the lab website here.