1. Bootstrapping intrinsically motivated learning with human demonstration
- Author
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Pierre-Yves Oudeyer, Sao Mai Nguyen, Adrien Baranes, Flowing Epigenetic Robots and Systems (Flowers), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), ERC Grant EXPLORERS 240007, Flowers, ANR-10-BLAN-0216,MACSi,Modèle pour l'Apprentissage du Comportement Sensorimoteur d'iCub(2010), European Project: 240007,EC:FP7:ERC,ERC-2009-StG,EXPLORERS(2009), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Unité d'Informatique et d'Ingénierie des Systèmes (U2IS), and École Nationale Supérieure de Techniques Avancées (ENSTA Paris)-École Nationale Supérieure de Techniques Avancées (ENSTA Paris)
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,0209 industrial biotechnology ,Intrinsic Motivation ,Computer Science - Artificial Intelligence ,Computer science ,[SCCO.COMP]Cognitive science/Computer science ,02 engineering and technology ,Human–robot interaction ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] ,Machine Learning (cs.LG) ,Computer Science - Robotics ,020901 industrial engineering & automation ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,Human–computer interaction ,0202 electrical engineering, electronic engineering, information engineering ,[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] ,Intrinsic motivation ,Imitation Learning ,Bootstrapping (linguistics) ,Learning by demonstration ,Social learning ,Social Learning ,Social relation ,Human Teacher ,Artificial Intelligence (cs.AI) ,Robot ,020201 artificial intelligence & image processing ,Learning by Demonstration ,Robotics (cs.RO) ,Guided learning - Abstract
This paper studies the coupling of internally guided learning and social interaction, and more specifically the improvement owing to demonstrations of the learning by intrinsic motivation. We present Socially Guided Intrinsic Motivation by Demonstration (SGIM-D), an algorithm for learning in continuous, 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., IEEE International Conference on Development and Learning, Frankfurt : Germany (2011)
- Published
- 2011
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