Events
Meet the Faculty Lecture: Brenna Argall

"Learning Robot Motion Control for Autonomous and Assistive Robots"
October 19, 2011
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Abstract: Demonstration learning is a powerful and practical technique to develop robot motion control behaviors, which can be further assisted by continuing to learn from experience after demonstration. This talk will overview an approach to learning robot motion control developed during my dissertation and postdoctoral research, that first acquires a control policy from teacher demonstration and then adapts the learned policy with corrective feedback. Validation of this approach has been carried out on in two very different robot domains: mobility control for a wheeled robot, and manipulation control for a high degree-of-freedom humanoid. Corrective feedback overall was found to improve the performance of a demonstrated behavior, as well as to enable its adaptation different motion control tasks. Future research directions in rehabilitation robotics, that will include the integration of this demonstration-correction approach with human-assistive devices, will be highlighted.

Bio: Prof. Brenna Argall is an Assistant Professor of Rehabilitation Robotics, with a joint appointment in the Department of Electrical Engineering and Computer Science (EECS) and the Department of Physical Medicine and Rehabilitation (PMR). She is coming from a postdoctoral fellowship in the Learning Algorithms and Systems Laboratory at the Swiss Federal Institute of Technology in Lausanne (EPFL). Her Ph.D. in Robotics (2009) was received from the Robotics Institute at Carnegie Mellon University, as well as her M.S. in Robotics (2006) and B.S. in Mathematics (2002).

Prior to graduate school, she held a Computational Biology position in the Laboratory of Brain and Cognition, at the National Institutes of Health. Her research interests lay at the intersection of robotics, machine learning and rehabilitation; in particular, on learning robot motion control from demonstration and then enriching behavior development with human feedback, with a focus on robotic devices that provides physical assistance.