Human-Centered AI for Accessible and Assistive Robotics: Towards a Disability-Centered HRI

Human-Centered AI for Accessible and Assistive Robotics: Towards a Disability-Centered HRI

Abstract: Powered by advances in AI, especially machine learning, robots are becoming smarter and more widely used.  Robots can provide critical assistance to people in a variety of contexts, from improving the efficiency of workers to helping people with disabilities in their day-to-day lives.  However, inadequate attention to the needs of users in developing these intelligent robots results in systems that are both less effective at their core tasks and more likely to do unintended harm.  The Assistive Agent Behavior and Learning (AABL) Lab at Tufts University seeks to apply human-centered design thinking, especially disability ethics, to the design of state-of-the-art robot learning algorithms and interaction frameworks.  This talk will explore how disability-community-centered thinking can be used to inspire new directions for intelligent interactive robotics and review recent work from the AABL lab at the intersection of assistive robotics, robot learning, and human-robot interaction.  


Bio: Elaine Schaertl Short is the Clare Boothe Luce Assistant Professor of Computer Science at Tufts University. She holds a PhD and MS in Computer Science at the University of Southern California (USC) and a BS in Computer Science from Yale University. Her research seeks to improve human-robot interaction by designing new algorithms that succeed in contexts where other algorithms’ assumptions frequently fail, such as in child-robot interaction, in public spaces and in assistive interactions.   She is equally committed human-centered research practices as she is to algorithm and robot design: she has co-authored more than 50 works on human-robot interaction spanning from designing a low-cost open-source open-hardware robot platform, to understanding family group interactions with socially assistive robots, to designing new neural network architectures for sim-to-real model transfer in robot learning.  Recently, Elaine led a team of researchers at Tufts who received a grant from the NSF National Robotics Initiative 3.0 with the goal of transforming our understanding of assistive robotics as a largely one-directional process in which the robot provides assistance to the user to a bi-directional process of mutually assistive robotics, in which assistance flows equally from user to robot and from robot to user. As a disabled faculty member, Elaine is particularly passionate about disability rights in her service work.  She is a co-PI of AccessComputing and co-Chair of AccessSIGCHI, an advocacy group that works to increase the accessibility of the 24 SIGCHI conferences.