Curiosity-Driven Development of Tool Use Precursors : a Computational Model

Studies of child development of tool use precursors show successive but overlapping phases of qualitatively different types of behaviours. We hypothesize that two mechanisms in particular play a role in the structuring of these phases: the intrinsic motivation to explore and the representation used to encode sensorimotor experience. Previous models showed how curiosity-driven learning mechanisms could allow the emergence of developmental trajectories. We build upon those models and present the HACOB (Hierarchical Active Curiosity-driven mOdel Babbling) architecture that actively chooses which sensorimotor model to train in a hierarchy of models representing the environmental structure. We study this architecture using a simulated robotic arm interacting with objects in a 2D environment. We show that overlapping phases of behaviours are autonomously emerging in hierarchical models using active model babbling. To our knowledge, this is the first model of curiosity-driven development of simple tool use and of the self-organization of overlapping phases of behaviours. In particular, our model explains why and how intrinsically motivated exploration of non-optimal methods to solve certain sensorimotor problems can be useful to discover how to solve other sensorimotor problems, in accordance with Siegler’s overlapping waves theory, by scaffolding the learning of increasingly complex affordances in the environment.

[1]  P. Zelazo,et al.  The emergence of functional play in infants: Evidence for a major cognitive transition , 1980 .

[2]  R. Siegler Emerging Minds: The Process of Change in Children's Thinking , 1996 .

[3]  Alexander Stoytchev,et al.  Behavior-Grounded Representation of Tool Affordances , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[4]  Pierre-Yves Oudeyer,et al.  Intrinsic Motivation Systems for Autonomous Mental Development , 2007, IEEE Transactions on Evolutionary Computation.

[5]  Jochen J. Steil,et al.  Goal Babbling Permits Direct Learning of Inverse Kinematics , 2010, IEEE Transactions on Autonomous Mental Development.

[6]  Pierre-Yves Oudeyer,et al.  Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner , 2012, Paladyn J. Behav. Robotics.

[7]  Pierre-Yves Oudeyer,et al.  Information-seeking, curiosity, and attention: computational and neural mechanisms , 2013, Trends in Cognitive Sciences.

[8]  Pierre-Yves Oudeyer,et al.  Active learning of inverse models with intrinsically motivated goal exploration in robots , 2013, Robotics Auton. Syst..

[9]  Dirk Kraft,et al.  A Survey of the Ontogeny of Tool Use: From Sensorimotor Experience to Planning , 2013, IEEE Transactions on Autonomous Mental Development.

[10]  Marco Mirolli,et al.  Intrinsically Motivated Learning in Natural and Artificial Systems , 2013 .

[11]  Jun Nakanishi,et al.  Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors , 2013, Neural Computation.

[12]  Pierre-Yves Oudeyer,et al.  Self-organization of early vocal development in infants and machines: the role of intrinsic motivation , 2014, Front. Psychol..

[13]  Yukie Nagai,et al.  Staged Development of Robot Skills: Behavior Formation, Affordance Learning and Imitation with Motionese , 2015, IEEE Transactions on Autonomous Mental Development.

[14]  B. Hayden,et al.  The Psychology and Neuroscience of Curiosity , 2015, Neuron.