Investigating the Impact of Experience on a User's Ability to Perform Hierarchical Abstraction

Nina M Moorman
Georgia Tech
Nakul Gopalan
Arizona State University
Aman Singh
Georgia Tech
Erin Botti
Georgia Tech
Mariah Schrum
Georgia Tech
Chuxuan Yang
Georgia Tech
Lakshmi Seelam
Georgia Tech
Matthew Gombolay
Georgia Tech
Paper Website

Paper ID 4

Nominated for Best Student Paper

Session 1. Human-Centered Robotics

Poster Session Tuesday, July 11

Poster 4

Abstract: The field of Learning from Demonstration enables end-users, who are not robotics experts, to shape robot behavior. However, using human demonstrations to teach robots to solve long-horizon problems by leveraging the hierarchical structure of the task is still an unsolved problem. Prior work has yet to show that human users can provide sufficient demonstrations in novel domains without showing the demonstrators explicit teaching strategies for each domain. In this work, we investigate whether non-expert demonstrators can generalize robot teaching strategies to provide necessary and sufficient demonstrations to robots zero-shot in novel domains. We find that increasing participant experience with providing demonstrations improves their demonstration’s degree of sub-task abstraction (p<.001), teaching efficiency (p<.001), and sub-task redundancy (p<.05) in novel domains, allowing generalization in robot teaching. Our findings demonstrate for the first time that non-expert demonstrators can transfer knowledge from a series of training experiences to novel domains without the need for explicit instruction, such that they can provide necessary and sufficient demonstrations when programming robots to complete task and motion planning problems.