Ph.D. Dissertation Defense Announcement

Title: Robots Learning Actions and Goals from Everyday People

Student:

Baris Akgun

Robotics Ph.D. Candidate

School of Interactive Computing

College of Computing

Georgia Institute of Technology

Date: Monday, October 26, 2015

Time:  12:00-2:00PM EST.

Place: TBA

Committee:

Dr. Andrea Thomaz (Advisor, School of Interactive Computing, Georgia Institute of Technology) Dr. Henrik Christensen (School of Interactive Computing, Georgia Institute of Technology) Dr. Charles Isbell  (School of Interactive Computing, Georgia Institute of Technology) Dr. Magnus Egerstedt (School of Electrical and Computer Engineering, Georgia Institute of Technology) Dr. Pieter Abbeel  (Department of Electrical Engineering and Computer Sciences, UC Berkeley)

Abstract:

Robots are destined to move beyond the caged factory floors towards domains where they will be interacting closely with humans. They will encounter highly varied environments, scenarios and user demands. As a result, programming robots after deployment will be an important requirement. To address this challenge, the field of Learning from Demonstration (LfD) emerged with the vision of programming robots through demonstrations of the desired behavior instead of explicit programming. The field of LfD within robotics has been around for more than 30 years and is still an actively researched field. However, very little research is done on the implications of having a non-robotics expert as a teacher. This thesis aims to bridge this gap by developing learning from demonstration algorithms and interaction paradigms that allow non-expert people to teach robots new skills.

The first step of the thesis was to evaluate how non-expert teachers provide demonstrations to robots. Keyframe demonstrations are introduced to the field of LfD to help people teach skills to robots and compared with the traditional trajectory demonstrations. The utility of keyframes are validated by a series of experiments. Based on the experiments, a hybrid of trajectory and keyframe demonstrations are proposed to take advantage of both and a method developed to learn from trajectories, keyframes and hybrid demonstrations in a unified way.

During the user experiments, it was observed that teacher were goal oriented. They concentrated on achieving the goal of the demonstrated skills rather than providing good quality demonstrations. Based on this observation, a method that can learn actions and goals from the same set of demonstrations is introduced. The action models are used to execute the skill and goal models to monitor this execution. A user study showed that successful goal models can be learned from non-expert teacher data even if the resulting action models are not as successful. Based on these results, a self-improvement method is developed that uses the goal monitoring output to improve the action models, without further user input. This approach is tested with an expert user and shown to be successful. Finally, an interactive version of the LfD system that incorporates both goal learning and self-improvement has been evaluated with naive users.