Abstract

Robot programming by demonstration (RPD) covers methods by which a robot learns new skills through human guidance. We present an interactive, multimodal RPD framework using active teaching methods that places the human teacher in the robot’s learning loop. Two experiments are presented in which observational learning is first used to demonstrate a manipulation skill to a HOAP-3 humanoid robot by using motion sensors attached to the teacher’s body. Then, putting the robot through the motion, the teacher incrementally refines the robot’s skill by moving its arms manually, providing the appropriate scaffolds to reproduce the action. An incremental teaching scenario is proposed based on insights from various fields addressing developmental, psychological, and social issues related to teaching mechanisms in humans. Based on this analysis, different benchmarks are suggested to evaluate the setup further. In a robot programming by demonstration (RPD) framework, a robot learns new skills through the help of a human instructor (Billard & Siegwart, 2004). Traditionally, RPD tends to consider the human user as an expert model who performs a task while the robot observes passively the demonstration (Ikeuchi & Suchiro, 1992; Kuniyoshi, Inaba, & Inoue, 1994). However, in humans, teaching is a social and bidirectional process in which teacher and learner are both active. Instead of considering the teacher solely as a model of successful expert behavior, recent work has referred to the teacher-learner couple as a We gratefully acknowledge Chrystopher L. Nehaniv, Aris Alissandrakis, Joe Saunders, Nuno Otero and Kerstin Dautenhahn for the useful exchanges of thoughts concerning social cues and feedback as well as the user experience and evaluation issues tackled by the Cogniron project. We would also like to acknowledge the four anonymous reviewers for their very useful comments on an earlier version of the manuscript.

Citations