Undoubtedly, reproducing features of animal locomotion on legged machines is a fascinating area of research. Yet, to realize the potential of legged robots in real-world applications, basic movement patterns must be coordinated and regulated to synthesize more complex behaviors that achieve desirable task-level objectives. This talk will present a hierarchical framework for synthesizing locomotion control and motion planning strategies, capable of translating “descending” task-level commands to suitable low-level control actions that harness the platform’s locomotion capabilities.
Our approach relies on the construction of a library of locomotion primitives in the form of suitably parameterized dynamical systems that capture the platform’s capabilities. Composition of these primitive behaviors then results in a wide range of locomotion behaviors that afford stability guarantees and can faithfully realize high-level objectives while the robot adapts to the environment’s geometric and dynamic constraints.
Prof. Ioannis Poulakakis earned his Ph.D. in Electrical Engineering from the University of Michigan in 2008. He served as a Postdoctoral Research Associate at Princeton University before joining the University of Delaware in 2010, where he currently serves as an Associate Professor of Mechanical Engineering.
His research interests are in dynamics and control with application to bio-inspired robotic systems, specifically legged robots. In 2014 he received a Faculty Early Career Development Award from the National Science Foundation to investigate task planning and motion control for legged robots at different scales.