PhD Candidate, Georgia Tech
Email
Abstract
Human-Guided Task Transfer in Interactive Robots
As robots become more commonplace, they will need to address a wide variety of problems. Since a robot cannot be programmed to complete every task, it is necessary for robots to learn new tasks by interacting with a human teacher. Learning from Demonstration is an effective method for this; however, current methods require that the robot receive many demonstrations of a task, or they are limited to completing tasks which are nearly identical to previous demonstrations.
In my research, I propose that the differences between a source environment (in which the task is demonstrated) and a target environment (containing new objects) lie on a spectrum of similarity. At one end of the spectrum, the source and the target tasks are identical so that the memory of previously learned skills directly supplies the answer for the target problem. In the middle of the spectrum, differences between the source and target tasks may be limited to minor modifications to object features or positions. Further along the spectrum, the differences between the source and target tasks may include significant modifications to object features and configurations, necessitating new action models to address the target problem.
I propose that a robot may address this range of transfer tasks by (i) analyzing the similarities and differences between the source and target problems, (ii) identifying the level of knowledge abstraction appropriate for transfer for the given type of similarity, and (iii) collaborating with a human teacher to ground the knowledge abstractions in the transfer task.
Bio
Tesca Fitzgerald is a Computer Science PhD candidate in the School of Interactive Computing at the Georgia Institute of Technology. Her research vision lies at the intersection of Human-Robot Interaction and Cognitive Systems. In her PhD, she has been developing algorithms and knowledge representations for robots to learn, adapt, and reuse task knowledge through interaction with a human teacher. In doing so, she applies concepts of social learning and cognition to develop a robot which adapts to human environments.
Before joining Georgia Tech in 2013, Tesca graduated from Portland State University with a B.Sc. in Computer Science. She is co-advised by Dr. Ashok Goel (director of the Design and Intelligence Lab) and Dr. Andrea Thomaz (director of the Socially Intelligent Machines Lab).
Tesca is an NSF Graduate Research Fellow (2014), Microsoft Graduate Women Scholar (2014), and IBM Ph.D. Fellow (2017).