Using Machine Teaching to Boost Novices' Robot Teaching Skill

Using Machine Teaching to Boost Novices' Robot Teaching Skill Abstract-- Recent evidence has shown that, contrary to expectations, it is difficult for users, especially novices, to teach robots tasks through learning from demonstration (LfD). This paper introduces a framework that leverages machine teaching algorithms to train novices to become better teachers of robots, and verifies whether such teaching ability is (i) retained beyond the period of training and (ii) generalises such that novices teach robots more effectively, even for skills for which training has not been received. A between-subjects study is reported, in which novice teachers are asked to teach simple motor skills to a robot. The results demonstrate that subjects that receive training show average 78.83% improvement in teaching ability (as measured by accuracy of the skill learnt by the robot), and average 63.69% improvement in the teaching of new skills not included as part of the training. The proposed approach allows Robot learning from demonstration (LfD) is a technology human teachers to be trained to teach robot dynamic motor that enables robots to learn tasks by observing and imitating skills using machine teaching.