CS Talk
Dr. Tesca Fitzgerald
Host: Brian Scassellati
Title: Learning to address novel situations through human-robot collaboration
Abstract
As our expectations for robots’ adaptive capacities grow, it will be increasingly important for
them to reason about the novel objects, tasks, and interactions inherent to everyday life. Rather
than attempt to pre-train a robot for all potential task variations it may encounter, we can
develop more capable and robust robots by assuming they will inevitably encounter situations
that they are initially unprepared to address. My work enables a robot to address these novel
situations by learning from a human teacher’s domain knowledge of the task, such as the
contextual use of an object or tool. Meeting this challenge requires robots to be flexible not only
to novelty, but to different forms of novelty and their varying effects on the robot’s task
completion. In this talk, I will focus on (1) the implications of novelty, and its various causes, on
the robot’s learning goals, (2) methods for structuring its interaction with the human teacher in
order to meet those learning goals, and (3) modeling and learning from interaction-derived
training data to address novelty.
Bio
Dr. Tesca Fitzgerald is a Postdoctoral Fellow in the Robotics Institute at Carnegie Mellon
University. Her research is centered around interactive robot learning, with the aim of developing
robots that are adaptive, robust, and collaborative when faced with novel situations. Before
joining Carnegie Mellon, Dr. Fitzgerald received her PhD in Computer Science at Georgia Tech
and completed her B.Sc at Portland State University. She is an NSF Graduate Research Fellow
(2014), Microsoft Graduate Women Scholar (2014), and IBM Ph.D. Fellow (2017).