Using perspective taking to learn from ambiguous demonstrations

• Published in: Robotics and autonomous systems 2006-05, Vol.54 (5), p.385-393
• Main Authors: Breazeal, Cynthia, Berlin, Matt, Brooks, Andrew, Gray, Jesse, Thomaz, Andrea L.
• Format: Article
• Language: English
• Subjects: Human–robot interactions; Learning by demonstration; Perspective taking; Socially guided learning
• ISSN: 0921-8890; 1872-793X
• DOI: 10.1016/j.robot.2006.02.004

This paper addresses an important issue in learning from demonstrations that are provided by “naïve” human teachers—people who do not have expertise in the machine learning algorithms used by the robot. We therefore entertain the possibility that, whereas the average human user may provide sensible demonstrations from a human’s perspective, these same demonstrations may be insufficient, incomplete, ambiguous, or otherwise “flawed” from the perspective of the training set needed by the learning algorithm to generalize properly. To address this issue, we present a system where the robot is modeled as a socially engaged and socially cognitive learner. We illustrate the merits of this approach through an example where the robot is able to correctly learn from “flawed” demonstrations by taking the visual perspective of the human instructor to clarify potential ambiguities.