Resilient, Adaptive, and Scalably Complex Soft Tensegrity Robots
Completely soft robots are emerging as a compelling new platform for
exploring and operating in unstructured, rugged, and dynamic
environments. In principle they promise a level of mobility,
resiliency, and configurability matched only by biological organisms.
Unfortunately, the very properties which make soft robots so appealing
also make them difficult to accurately model, scalably design, and
robustly control. This talk will introduce tensegrity-based robots
as a compelling low-cost entry-level platform with which to explore
the fundamental soft robotic challenges of design and control.
Despite their relative simplicity, tensegrities are an effective
substrate for soft robotics research: they are continuum structures
with a high dimensional configuration space; their pre-stress
results in high compliance, and they are highly configurable --
capable of rapid shape change and tunable stiffness. This talk will
show how we can employ state of the art machine learning techniques to
discover robust and dynamic behaviors which exploit, rather than
suppress, complex soft body dynamics. I will also show how to
leverage automated design tools in to invent self-assembling soft
robots which can scale in size and complexity.
John Rieffel is an Associate Professor in the Computer Science Department at Union College in Schenectady, NY. His research expertise is in soft robotics and genetic algorithms. He received in undergraduate degrees in Engineering (BS) and Computer Science (BA) from Swarthmore College, and his Ph.D. in Computer Science from Brandeis University. He has been a postdoctoral associate in Hod Lipson's lab at Cornell University, and in Barry Trimmer's lab at Tufts University.