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Translating animal movement into better robotic design

Revzen believes that his findings can be used to engineer better man-made devices, including prosthetic limbs and complete robots.

Professor Shai Revzen Enlarge
Professor Shai Revzen

Prof. Shai Revzen pioneered a method, called Data Driven Floquet Analysis (DDFA), which he is currently using to test scientific theories of neuromechanical control in animals and humans, and extract principles that may guide future robotic design. This research is funded by the Army Research Office as part of the Young Investigator Program (YIP). The project is called, “Data Driven Floquet Analysis: Nonlinear Oscillators in Locomotion.”

From his background in Biomechanics, Prof. Revzen knows that there is currently no complete understanding of why a particular gait is used by an animal at any given context, nor of what the advantages and disadvantages of various gaits may be. Once this is known, he believes that the information can be used to engineer better man-made devices, including prosthetic limbs and complete robots.

Prof. Shai Revzen’s research focuses on the role of mechanical dynamics in the control of animal and robot motion. He hopes to identify, model and reproduce the strategies animals use to combine mechanical and neural control for interacting with physical objects.

Prof. Revzen officially joined the faculty at the University of Michigan last September 2012. He received his PhD in Integrative Biology (Biomechanics) from the University of California – Berkeley in 2009, and more recently was a postdoctoral researcher in the General Robotics, Automation, Sensing and Perception (GRASP) Lab of the University of Pennsylvania. Shai is a founding partner of Bio-Signal Analysis, an electrocardiology technology start-up company.

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Autonomy, AI & Robotics; Control Systems; Research News; Robotics and Autonomous Systems; Shai Revzen