Communication and knowledge sharing in human–robot interaction and learning from demonstration

• Published in: Neural networks Vol. 23; no. 8; pp. 1104 - 1112
• Main Authors: Koenig, Nathan, Takayama, Leila, Matarić, Maja
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.10.2010
• Subjects: Acoustic Stimulation; Adult; Artificial Intelligence; Auditory; Communication; Computer Graphics; Cues; Feedback, Psychological; Female; Humans; Knowledge; Learning - physiology; Learning from demonstration (LfD); Male; Middle Aged; Neuropsychological Tests; Photic Stimulation; Robotics; Social Environment; Teaching; User-Computer Interface; Visual; Young Adult; Auditory; Learning from demonstration (LfD); Visual; Teaching
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2010.06.005

Inexpensive personal robots will soon become available to a large portion of the population. Currently, most consumer robots are relatively simple single-purpose machines or toys. In order to be cost effective and thus widely accepted, robots will need to be able to accomplish a wide range of tasks in diverse conditions. Learning these tasks from demonstrations offers a convenient mechanism to customize and train a robot by transferring task related knowledge from a user to a robot. This avoids the time-consuming and complex process of manual programming. The way in which the user interacts with a robot during a demonstration plays a vital role in terms of how effectively and accurately the user is able to provide a demonstration. Teaching through demonstrations is a social activity, one that requires bidirectional communication between a teacher and a student. The work described in this paper studies how the user’s visual observation of the robot and the robot’s auditory cues affect the user’s ability to teach the robot in a social setting. Results show that auditory cues provide important knowledge about the robot’s internal state, while visual observation of a robot can hinder an instructor due to incorrect mental models of the robot and distractions from the robot’s movements.