Bootstrapping intrinsically motivated learning with human demonstrations

Abstract—This paper studies the coupling of internally guided learning and social interaction, and more specifically the im-provement owing to demonstrations of the learning by intrinsic motivation. We present Socially Guided Intrinsic Motivation by Demonstration (SGIM-D), an algorithm for learning in continu-ous, unbounded and non-preset environments. After introducing social learning and intrinsic motivation, we describe the design of our algorithm, before showing through a fishing experiment that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation to gain a wide repertoire while being specialised in specific subspaces. I. APPROACHES FOR ADAPTIVE PERSONAL ROBOTS The promise of personal robots operating in human environ-ments to interact with people on a daily basis points out the importance of adaptivity of the machine to its environment an