Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic and two-pronged: on the one hand, it employs robots to instantiate models originating from developmental sciences; on the other hand, it aims to develop better robotic systems by exploiting insights gained from studies on ontogenetic development. This paper gives a survey of the relevant research issues and points to some future research directions.

The agent-based approach emphasizes the importance of learning through organism-environment interaction. This approach is part of a recent trend in computational models of learning and development toward studying autonomous organisms that are embedded in virtual or real environments. In this paper we introduce the concepts of online and offline sampling and highlight the role of online sampling in agent-based models. After comparing the strengths of each approach for modeling particular developmental phenomena and research questions, we describe a recent agent-based model of infant causal perception. We conclude by discussing some of the present limitations of agent-based models and suggesting how these challenges may be addressed. © 2001 Academic Press Computational models of learning and development are playing an increasingly critical role in child development research (Cassidy, 1990;

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Trial-and-error learning strategies play a central role in sensorimotor development during early infancy. However, learning to reach by trial-and-error normally requires a slow and laborious search through the space of possible movements. We propose a computational model of reaching based on the notion that early sensorimotor control is driven by the generation of exploratory movements, followed by the selection and maintenance of adaptive movement patterns. We find that instead of exhaustively exploring the full search space of movement patterns, the model exploits several emergent constraints that limit the initial size of the movement search space. These constraints exploit both mechanical and kinematic properties of the reaching task. We relate these results to the development of reaching during infancy, and discuss recent findings that have identified similar constraints in young infants. Learning to Reach 3 Learning to Reach by Constraining the Movement Search Space Several rec...

What is the enactive approach to cognition? Over the last 15 years this banner has grown to become a respectable alternative to traditional frameworks in cognitive science. It is at the same time a label with different interpretations and upon which different doubts have been cast. This paper elaborates on the core ideas that define the enactive approach and their implications: autonomy, sensemaking, emergence, embodiment, and experience. These are coherent, radical and very powerful concepts that establish clear methodological guidelines for research. The paper also looks at the problems that arise from taking these ideas seriously. The enactive approach has plenty of room for elaboration in many different areas and many challenges to respond to. In particular, we concentrate on the problems surrounding several theories of value-appraisal and valuegeneration. The enactive view takes the task of understanding meaning and value very seriously and elaborates a proper scientific alternative to reductionist attempts to tackle these issues by functional localization. Another area where the enactive framework can make a significant contribution is social interaction and

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Abstract — This paper describes a developmental system implemented on a real robot that learns a model of its own sensory and actuator apparatuses. There is no innate knowledge regarding the modality or representation of the sensoric input and the actuators, and the system relies on generic properties of the robot’s world such as piecewise smooth effects of movement on sensory changes. The robot develops the model of its sensorimotor system by first performing random movements to create an informational map of the sensors. Using this map the robot then learns what effects the different possible actions have on the sensors. After this developmental process the robot can perform simple motion tracking. Index Terms — developmental robotics, body babbling, emergence of structure I.

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Novel architectures for behaviour focus on selforganization and the possibilities of environmentally situated sensory-motor systems. How well does this perspective extend to understanding development ? Two approaches to unsupervised behavioural acquisition that are of current interest to roboticists are considered: selection from the system's own activities, based on bias by evolutionarily determined value schemes; and imitation through observation of the behaviour of more competent others. Work with robots is compared with studies of naturally intelligent primates, including human infants, demonstrating the primacy of `learning by doing' to both accounts of behavioural acquisition. 1 Goals for changing systems For many traditional domains of inquiry, work in artificial life is offering a radically new view of problems and potential solutions, unified by revealing how complex phenomena can emerge from interaction of many simple processes when an appropriate scale of analysis is adopt...

Abstract. Motor synergies, i.e. systematic relations between effectors, have been first proposed as a principle in motor control by N. Bernstein in 1935. Thereafter, his idea has inspired many models of motor control in humans and animals. Recently, “linear synergy”, i.e. a linear relation between the torques applied to different joints, was reported to occur in human subjects during directional pointing movements [4]. In this paper, results from experiments in evolutionary robotics are presented to explore the concept of synergies in general and the role of linear synergy in the organisation of movement in particular. A 3D simulated robotic arm is evolved to reach to different target spots on a plane. Linear synergy is not found to be an outcome of the evolutionary search process, but imposing linear synergy as a constraint on artificial evolution dramatically improves evolvability and performance of evolved controllers. 1 Motor Synergies The centipede was happy quite,