Mechanical Engineering Special Seminar Series
Reduction and Identification for Models of Locomotion: an Emerging Systems Theory for Neuromechanics
Speaker: Sam Burden
Electrical Engineering and Computer Sciences
University of California, Berkeley
Legged animals are dynamic and dexterous, whereas their robot counterparts are often slow and clumsy. A central challenge in the design of such neuromechanical systems arises from the interaction of the nervous system with an environment through limbs. By focusing on the piecewise-defined ("hybrid") dynamics governing locomotion and manipulation, I prove that this interaction generically leads to reduction in the number of mechanical degrees-of-freedom in models of periodic gaits.
Furthermore, I exploit the structure of the reduced-order dynamics to derive a scalable algorithm for parameter identification using motion capture data and apply this technique to study perturbation recovery in running cockroaches. Finally, I combine these analytical, computational, and experimental tools to propose a foundation for systematically engineering neuromechanics. In ongoing and future work, I pursue applications in robotics and rehabilitation.
Sam Burden earned his BS with Honors in Electrical Engineering from the University of Washington, Seattle. Currently, he is a PhD candidate in Electrical Engineering and Computer Sciences at the University of California, Berkeley and expects to graduate in May of 2014. He is broadly interested in applying control and dynamical systems theory to study neuromechanical and cyberphysical systems. Specifically, he focuses on discovering and formalizing principles that enable dynamic locomotion and dexterous manipulation in robotics, biomechanics, and human motor control. He is a recipient of the NSF Graduate Research Fellowship and collaborator in the ARL Micro Autonomous Systems and Technology CTA. In his spare time, he teaches robotics to students of all ages in K-12 classrooms, Maker Fairs, and campus events.
Link to PDF flyer.