Abstract

As we begin to deploy more and more robots in the field, we encounter a growing need for both autonomous recovery from failure, and for graceful degradation under damage. Both of these are properties of biological systems. Recently we have shown two different approaches that employ tools and insights from the mathematical modeling of animal locomotion to allow our robots to quickly recover from typical failures. 
In one approach, our investigation revealed that the physics of multilegged running are a lot closer to swimming in low Reynolds number (Stokesian) fluids than they are to human running. By exploiting this fact, our robots could be made to learn how to move with only a few minutes of physical trials.  When a failure occurs, the robots re-learn how to move even faster.
In another approach, the reformulation of robot dynamics in terms of simultaneous constraints allowed us to exploit the observation that many common failures are low-rank in terms of the constraints.  By augmenting the constraints that survived the failure with a naive learning algorithm, our robots quickly re-learned how to perform the desired behavior. 
Both approaches suggest that we are moving closer to animal-like abilities of recovery from damage.

Bio

Shai Revzen is an Assistant Professor of Electrical Engineering and Computer Science in the College of Engineering, and holds a courtesy faculty appointment in the Department of Ecology and Evolutionary Biology in the College of Literature, Science and the Arts. He received his PhD in Integrative Biology doing research in the PolyPEDAL Lab at the University of California at Berkeley, and did his postdoctoral work in the GRASP Laboratory of the University of Pennsylvania. Prior to his academic work, Shai was Chief Architect R&D of the convergent systems division of Harmonic Lightwaves (HLIT), and a co-founder of Bio-Systems Analysis, a biomedical technology start-up.

As principal investigator of the Biologically Inspired Robotics and Dynamical Systems (BIRDS) lab, Shai sets the research agenda and innovative tone of the lab. He believes in the intrinsic value of fundamental science, and of its transformative potential for robotics and future technology. Under his supervision, the lab combines work in three disciplines: robotics, mathematics, and biology.