Mike RubensteinAssistant Professor of Computer Science Assistant Professor of Mechanical EngineeringThe Lisa Wissner-Slivka and Benjamin Slivka Professor in Computer Science
My research interest is to advance the control and design of multi-robot systems, enabling their use instead of traditional single robots and to solve problems for which traditional robots are not suitable. Using these multi-robot systems can offer more parallelism, adaptability, and fault tolerance when compared to a traditional single robot. I am also interested in investigating how new technologies will allow for more capable multi-robot systems, and how these technologies impact the design of multi-robot algorithms, especially as these systems begin to number in the hundreds, thousands, or even millions of robots.
I received my PhD from the University of Southern California in Computer Science. After my PhD, I was a postdoctoral researcher in the Self-Organizing Systems Research Group at Harvard University. I completed my undergraduate degree in Electrical Engineering at Purdue University.
Ph.D Computer Science, University of Southern California, Los Angeles, CA
M.S. Electrical Engineering, University of Southern California, Los Angeles, CA
B.S. Electrical Engineering, Purdue University, West Lafayette, IN
EECS 496/496, ME 495 Swarms and Multi-Robot Systems
Advances in technology have begun to allow for the production of large groups, or swarms, of robots; however, there exists a large gap between their current capabilities and those of swarms found in nature or envisioned for future robot swarms. These deficiencies are the result of two factors, difficulties in algorithmic control of these swarms, and limitations in hardware capabilities of the individuals. This class surveys the state of the art research that addresses these deficiencies. Coursework includes reading research papers, student presentations and discussion of select papers, and a final project implementing studied topics in a real or simulated robot swarm.
EECS 496/496, ME 495 Applied Mechatronics: Quadrotor Control and Design
Centered around a project where teams (1 to 2 students per team) create and program a visually-guided autonomous quadrotor robot, this class focuses on advanced embedded control of an electromechanical system. Topics include mechanical/electrical assembly, programming interfaces between an embedded computer (such as a Raspberry PI running Linux) and external sensors/actuators (such as an IMU, camera, or motor controllers), programming and tuning a timing-critical control loop for stable flight, using pre-existing computer vision software for tracking optical targets, and creating a software stack that interacts with low-level code to create a desired high level behavior.
EECS 348 Intro to Artificial Intelligence
The goal of this course is to expose students to the basic ideas, challenges, techniques, and problems in artificial intelligence. Topics include strong (knowledge-based) and weak (search-based) methods for problem solving and inference, and alternative models of knowledge and learning, including symbolic, statistical and neural networks.