Special Issue on Robot Learning from Demonstration

Abstract

Algorithms for robot Learning from Demonstration (LfD) seek to enable human users to expand the capabilities of robots through interactive teaching instead of explicit programming. This special issue includes a collection of articles that span some of the issues and challenges that arise when LfD is done in the context of a social interaction with a human partner. The importance of designing algorithms and interactions with non-expert end-users in the loop is highlighted in the article by Suay et al. In one of the first comparative user studies of its kind, non-experts used and evaluated three different learning by demonstration algorithms/interactions in the same domain. Their results point out the non-trivial challenges that arise in getting learning input from a non-expert human teacher. One common problem domain tackled with Learning from Demonstration is that of learning low-level motion control policies to achieve some particular task or skill. Several of the articles in this special issue are working in this domain, touching on various important questions that come to bare when this type of LfD is done in a social context with a human teacher. In their article, Grollman et al. address an important topic for social LfD, learning from failed demonstrations. When demonstrations are to come from naive non-expert humans,