Smoothing contact-rich dynamics using morphological computation

Smoothing contact-rich dynamics using morphological computation

Soft Condensed Matter & Physics of Living Systems

Date

February 28, 2017 -
3:00pm to 4:00pm

Location

Klaus

Room

1116 West

Speaker

Affiliation

University of Washington

To move over, around, or through obstacles in the world, robots and animals need to employ a repertoire of dynamic and dexterous behaviors.  Since the world is ever-changing, these behaviors must be synthesized on-the-fly and adapted to diverse environmental conditions.  At present, animals deftly outperform autonomous robots in this regard.  We seek tools that will enable the performance of dynamic legged robots to surpass that of their animal counterparts.

In this talk, we discuss advances in modeling and control of dynamic legged locomotion.  Unlike some areas of robotics and biomechanics, models for most dynamic legged behaviors have poor predictive power.  In particular, rigid-body models of legged locomotion yield predictions that vary discontinuously when multiple limbs contact terrain.  By introducing compliance in hips and feet, we show that model predictions vary smoothly with respect to initial conditions (including states, parameters, and inputs). 

Smooth model predictions are amenable to scalable algorithms for estimation, optimization, and learning; we briefly discuss our current efforts and future plans in these directions.  We conclude that compliance in hips and feet perform morphological computations that can simplify modeling and control of dynamic legged locomotion.

BIOGRAPHY

Sam Burden earned his BS with Honors in Electrical Engineering from the University of Washington in Seattle in 2008.  He earned his PhD in Electrical Engineering and Computer Sciences from the University of California in Berkeley in 2014, where he subsequently spent one year as a Postdoctoral Scholar.  In 2015, he returned to UW EE as an Assistant Professor; in 2016, he received a Young Investigator award from the Army Research Office (ARO-YIP).  Sam is broadly interested in discovering and formalizing principles of sensorimotor control.  Specifically, he focuses on applications in dynamic and dexterous robotics, neuromechanical motor control, and human-cyber-physical systems.  In his spare time, he teaches robotics to students of all ages in classrooms and campus events.