PLEASE NOTE THE ROOM CHANGE - WE WILL MEET AT THE MCCORMICK BUILDING (TECH INSTITUTE) IN LECTURE ROOM 2 (LR2).
Demonstration-based 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. The first part of this talk will provide a crash course in the area of Robot Learning from Demonstration (LfD). The second part will overview an approach developed in my research, that acquires a motion 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 has been found to improve the performance of a demonstrated behavior, as well as to enable its adaptation different motion control tasks.
Brenna Argall is the June and Donald Brewer Junior Professor of Electrical Engineering and Computer Science at Northwestern University (NU). She also holds a Research Scientist position within the Sensory Motor Performance Program at the Rehabilitation Institute of Chicago (RIC), and is an assistant in the Department of Physical Medicine and Rehabilitation (PMR) at NU. Prior to joining Northwestern and RIC, she was a postdoctoral fellow [masked]) in the Learning Algorithms and Systems Laboratory at the École Polytechnique Fédérale de 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 (NIH). 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.