From Human Data to Bipedal Robotic Walking and Beyond - Rehabilitation Institute of Chicago

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Thu, Mar 24

Speaker: Aaron Ames, PhD (Texas A&M University; faculty candidate)

Title: From Human Data to Bipedal Robotic Walking and Beyond

Abstract: Humans have the amazing ability to walk with deceptive ease, navigating everything from daily environments to uneven and uncertain terrain with efficiency and robustness. If these same abilities can be imbued into robotic devices, the potential applications are far-reaching: from legged robots for space exploration to the next generation of prosthetic devices.

The purpose of this talk is to present the process of achieving human-like bipedal robotic walking by looking to humans, and specifically human walking data, to design formal models and controllers. The fundamental principle behind this process is that regardless of the complexity present in human walking—hundreds of degrees of freedom coupled with highly nonlinear dynamics and forcing—the essential information needed to understand walking is encoded in simple functions of the kinematics, or “outputs,” of the human, and this fundamental principle can be applied to obtain both models and controllers. At the level of models, we find that all humans display the same temporal ordering of events, or contact points, throughout a walking gait; this information uniquely determines a mathematical hybrid system model for a given bipedal robot. At the level of controllers, we find that humans display simple behavior for certain canonical “output” functions; by designing controllers that achieve the same output behavior in robots, we are able to achieve surprisingly human-like dynamic walking. The method used to achieve this walking allows for extensions beyond robotic walking; it can be used to quantify how “human-like” a walking gait is, and has potential applications to the design and simulation of controllers for prosthetic devices.


Aaron D. Ames is an Assistant Professor in Mechanical Engineering at Texas A&M University. His research interests center around dynamical, control and hybrid systems, with a special emphasis on bipedal robots, behavior unique to hybrid systems, such as Zeno behavior, and the mathematical foundations of hybrid systems.

Dr. Ames received a BS in Mechanical Engineering and a BA in Mathematics from the University of St. Thomas in 2001, and he received a MA in Mathematics and a PhD in Electrical Engineering and Computer Sciences from UC Berkeley in 2006. At UC Berkeley, he was the recipient of the 2005 Leon O. Chua Award for achievement in nonlinear science and the 2006 Bernard Friedman Memorial Prize in Applied Mathematics. Dr. Ames served as a Postdoctoral Scholar in the Control and Dynamical System Department at the California Institute of Technology from 2006 to 2008. In 2010 he received both the Norman Hackerman Award and the NSF CAREER award for his research on bipedal robotic walking and its applications to prosthetic devices.

Host: William Z. Rymer