Intrinsically Motivated Goal Exploration in Child Development and Artificial Intelligence : Learning and Development of Speech and Tool Use

Overview & Synopsis

Babies and children are curious, active explorers of their world. One of their challenges is to learn of the relations between their actions such as the use of tools or speech, and the changes in their environment. Intrinsic motivations have been little studied in psychology, such that its mechanisms are mostly unknown. On the other hand, most artificial agents and robots have been learning in a way very different from humans. The objective of this thesis is twofold: understanding the role of intrinsic motivations in human development of speech and tool use through robotic modeling, and improving the abilities of artificial agents inspired by the mechanisms of human exploration and learning. A first part of this work concerns the understanding and modeling of intrinsic motivations. We reanalyze a typical tool-use experiment, showing that intrinsically motivated exploration seems to play an important role in the observed behaviors and to interfere with the measured success rates. With a robotic model, we show that an intrinsic motivation based on the learning progress to reach goals with a modular representation can self-organize phases of behaviors in the development of tool-use precursors that share properties with child tool-use development. We present the first robotic model learning both speech and tool use from scratch, which predicts that the grounded exploration of objects in a social interaction scenario should accelerate infant vocal learning of accurate sounds for these objects' names as a result of a goal-directed exploration of the objects. In the second part of this thesis, we extend, formalize and evaluate the algorithms designed to model child development, with the aim to obtain an efficient learning robot. We formalize an approach called Intrinsically Motivated Goal Exploration Processes (IMGEP) that enables the discovery and acquisition of large repertoires of skills. We show within several experimental setups including a real humanoid robot that learning diverse spaces of goals with intrinsic motivations is more efficient for learning complex skills than only trying to directly learn these complex skills.