Constraining the Size Growth of the Task Space with Socially Guided Intrinsic Motivation using Demonstrations.

Sao Mai Nguyen (1) , Adrien Baranes (1) , Pierre-Yves Oudeyer (1)

1 Flowers - Flowing Epigenetic Robots and Systems

This paper presents an algorithm for learning a highly redundant inverse model in continuous and non-preset environments. Our Socially Guided Intrinsic Motivation by Demonstrations (SGIM-D) algorithm combines the advantages of both social learning and intrinsic motivation, to specialise in a wide range of skills, while lessening its dependence on the teacher. SGIM-D is evaluated on a fishing skill learning experiment.

Mots clés: Intrinsic Motivation; Social Learning; Imitation Learning; Learning by Demonstration; Large Spaces; Goal-Directed Exploration; Active Learning

Domaines: Intelligence artificielle [cs.AI]; Informatique; Psychologie; Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]; Robotique [cs.RO]; Apprentissage [cs.LG]

Sao Mai Nguyen, Adrien Baranes, Pierre-Yves Oudeyer. Constraining the Size Growth of the Task Space with Socially Guided Intrinsic Motivation using Demonstrations.. IJCAI Workshop on Agents Learning Interactively from Human Teachers (ALIHT), 2011, Barcelona, Spain. ⟨hal-00645995⟩