Tesca Fitzgerald

Tesca Fitzgerald

I am a Computer Science PhD candidate in the School of Interactive Computing at the Georgia Institute of Technology.

My research vision lies at the intersection of Human-Robot Interaction and Cognitive Systems. I develop algorithms and knowledge representations for robots to learn, adapt, and reuse task knowledge through interaction with a human teacher. In doing so, I apply concepts of social learning and cognition to a robot which adapts to human environments.

I am co-advised by Dr. Ashok Goel (director of the Design and Intelligence Lab) and Dr. Andrea Thomaz (director of the Socially Intelligent Machines Lab).

Before joining Georgia Tech in 2013, I graduated from Portland State University with a B.Sc. in Computer Science.




Dissertation Summary

Presented at the Georgia Tech 3 Minute Thesis competition

Video accompanying our AAMAS’19 paper titled

“Human-guided Trajectory Adaptation for Tool Transfer”

Publications are listed [here].

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.

Furthermore, I am developing a cognitive system based on case-based analogical learning that may enable a robot to collaborate with a human teacher to transfer task knowledge to a range of target problems. Given task demonstrations in source domains, this system stores the task knowledge as individual cases in memory. When given a target problem, it retrieves a similar case from memory, and then identifies the level of abstraction at which knowledge from the source case should be transferred to the target problem.