Human and Machine Learning for Assistive Autonomy
Abstract
Brenna Argall
As need increases, access decreases. It is a paradox that as human motor impairments become more severe, and increasing assistance needs are paired with decreasing motor abilities, the very machines created to provide this assistance become less and less accessible to operate with independence. My lab addresses this paradox by incorporating robotics autonomy and intelligence into physically-assistive machines: leveraging robotics autonomy, to advance human autonomy. Achieving the correct allocation of control between the human and the autonomy is essential, and critical for adoption. The allocation must be responsive to individual abilities and preferences, that moreover can be changing over time, and robust to human-machine information flow that is filtered and masked by motor impairment and control interface. As we see time and again in our work and within the field: customization and adaptation are key, and so the opportunities for machine learning are clear. However, the manner of its implementation is not. In this talk, I will discuss the needs of and need for machine learning within the domain of assistive machines that bridge gaps in human function, and overview ongoing efforts within my lab that aim to tackle adaptation and learning in its many forms.
Panelists
Aude Billard |
Emma Brunskill |
Finale Doshi-Velez |
Speaker Bio
Brenna Argall is an associate professor of Mechanical Engineering, Computer Science, and Physical Medicine & Rehabilitation at Northwestern University. She is director of the assistive & rehabilitation robotics laboratory (argallab) at the Shirley Ryan AbilityLab (formerly the Rehabilitation Institute of Chicago), the #1 ranked rehabilitation hospital in the United States. The mission of the argallab is to advance human ability by leveraging robotics autonomy. Argall is a 2016 recipient of the NSF CAREER award, and was named one of the 40 under 40 by Crain’s Chicago Business. Her Ph.D. in Robotics (2009) was received from the Robotics Institute at Carnegie Mellon University, as well as her B.S. in Mathematics (2002). Prior to joining Northwestern and RIC, she was a postdoctoral fellow (2009-2011) at the École Polytechnique Fédérale de Lausanne (EPFL), and prior to graduate school (2002-2004) she held a Computational Biology position at the National Institutes of Health (NIH). More recently, she was a visiting fellow at the Wyss Center for Bio and Neuroengineering in Geneva, Switzerland (2019).