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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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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: CCB 340
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 was developed to learn from trajectories, keyframes and hybrid demonstrations in a unified way.
A key insight from these user experiments was that teachers are goal oriented. They concentrated on achieving the goal of the demonstrated skills rather than providing good quality demonstrations. Based on this observation, this thesis introduces a method that can learn actions and goals from the same set of demonstrations. 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. Following these results, this thesis further develops a self-improvement 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, this thesis builds an interactive LfD system that incorporates both goal learning and self-improvement and evaluates it with naive users.
