This Frontiers Research Topic focuses on the question: Can we develop computers or robots that play and develop like children? Approaches to this question involves the elaboration and study of computational models of infant play with the perspective of two complementary disciplines. Firstly, developmental psychology benefits from such models to formulate theories and conjectures of infant play which can be tested and evaluated through experimental studies. Secondly, the new field of developmental robotics looks toward infant development for inspiration, data, and guidance, in order to build models of learning that may be useful both for better understanding of human development and for engineering autonomous learning in robots and other systems.
These fields have common ground in this very active and significant research area, investigating how babies learn and grow cognitively, and testing our knowledge in the concrete world of computer models. A major characteristic of early human development is the open-ended acquisition of new abilities and competencies. Human infants are born helpless yet they actively become familiar with their environment and their own body through spontaneous exploration and interaction with others. Within a few months of rapid learning and development, they have acquired quite sophisticated sensory-motor and social competences. New skills appear to sprout from current competences as experience builds along a continuous trajectory of action and interaction. In particular, such open-ended learning is readily seen in the ubiquitous behaviour known as play.
Play can be used to describe an expansive range of exploratory activities, but the concept currently lacks a sufficiently unifying theoretical framework. Here we focus on forms of play which involve free and spontaneous intrinsically motivated exploration of actions, objects, places or tasks and activities in varying contexts, outside motivation to fulfill basic physiological needs like feeding and without external goals set by social peers. Such forms of exploration may involve the search for novelty or surprise, can be goal-free but also involve self-generated goals which are pursued for their intrinsic “interestingness”. For example, when encountering novel objects or events, infants will often display pleasure in the interaction, try to repeat the experience and show enjoyment of their own activity. This suggests an enactive approach which Jerome Bruner called “learning by doing”. Von Hofsten describes play as “the purposeful seeking of enjoyable action possibilities”, and Vicky Bruce stresses the immersive aspects in terms of several features of “free-flow” play.
From developmental robotics, work on these ideas have explored both solitary play with objects and early interactive play with others as a generative behaviour that combines fragments of past experience with new sensory-motor events in differing contexts.
Computational models of play have been proposed, for example based on forms of novelty or information gain as an intrinsic driver, leading to designs for investigations on “curious robots”.
The aim of this Frontiers Research Topic is to present international state-of-the-art research from naturalistic or experimental infant studies and computational/robot modelling, on early infant play behaviour. The focus will be on the very earliest forms of play, because this is concurrent with increasing perception and understanding of the “physics of the world”, e.g. perceptions of objects, causality, and interactions. Many interesting questions arise: for example, how does play emerge and what is its relation to goal-free motor babbling? How does play relate to object understanding and world knowledge. How does intrinsically motived self-generation of goals relate to future extrinsically motivated goal generation and goal attribution? How far can the world be explored through the paradigm of play? How can we best understand more about infant cognition from modelling these concepts on robots? We solicit leading contributions eliciting experience and original research on computational modelling of psychological experiments about these topics, as well as experimental and theoretical papers that increase understanding of these important issues and core concepts in infants and machines.
This Frontiers Research Topic focuses on the question: Can we develop computers or robots that play and develop like children? Approaches to this question involves the elaboration and study of computational models of infant play with the perspective of two complementary disciplines. Firstly, developmental psychology benefits from such models to formulate theories and conjectures of infant play which can be tested and evaluated through experimental studies. Secondly, the new field of developmental robotics looks toward infant development for inspiration, data, and guidance, in order to build models of learning that may be useful both for better understanding of human development and for engineering autonomous learning in robots and other systems.
These fields have common ground in this very active and significant research area, investigating how babies learn and grow cognitively, and testing our knowledge in the concrete world of computer models. A major characteristic of early human development is the open-ended acquisition of new abilities and competencies. Human infants are born helpless yet they actively become familiar with their environment and their own body through spontaneous exploration and interaction with others. Within a few months of rapid learning and development, they have acquired quite sophisticated sensory-motor and social competences. New skills appear to sprout from current competences as experience builds along a continuous trajectory of action and interaction. In particular, such open-ended learning is readily seen in the ubiquitous behaviour known as play.
Play can be used to describe an expansive range of exploratory activities, but the concept currently lacks a sufficiently unifying theoretical framework. Here we focus on forms of play which involve free and spontaneous intrinsically motivated exploration of actions, objects, places or tasks and activities in varying contexts, outside motivation to fulfill basic physiological needs like feeding and without external goals set by social peers. Such forms of exploration may involve the search for novelty or surprise, can be goal-free but also involve self-generated goals which are pursued for their intrinsic “interestingness”. For example, when encountering novel objects or events, infants will often display pleasure in the interaction, try to repeat the experience and show enjoyment of their own activity. This suggests an enactive approach which Jerome Bruner called “learning by doing”. Von Hofsten describes play as “the purposeful seeking of enjoyable action possibilities”, and Vicky Bruce stresses the immersive aspects in terms of several features of “free-flow” play.
From developmental robotics, work on these ideas have explored both solitary play with objects and early interactive play with others as a generative behaviour that combines fragments of past experience with new sensory-motor events in differing contexts.
Computational models of play have been proposed, for example based on forms of novelty or information gain as an intrinsic driver, leading to designs for investigations on “curious robots”.
The aim of this Frontiers Research Topic is to present international state-of-the-art research from naturalistic or experimental infant studies and computational/robot modelling, on early infant play behaviour. The focus will be on the very earliest forms of play, because this is concurrent with increasing perception and understanding of the “physics of the world”, e.g. perceptions of objects, causality, and interactions. Many interesting questions arise: for example, how does play emerge and what is its relation to goal-free motor babbling? How does play relate to object understanding and world knowledge. How does intrinsically motived self-generation of goals relate to future extrinsically motivated goal generation and goal attribution? How far can the world be explored through the paradigm of play? How can we best understand more about infant cognition from modelling these concepts on robots? We solicit leading contributions eliciting experience and original research on computational modelling of psychological experiments about these topics, as well as experimental and theoretical papers that increase understanding of these important issues and core concepts in infants and machines.
