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;

Experimental results in psychology have shown the important role of manipulation in guiding infant development. This has inspired work in developmental robotics as well. In this case, however, the benefits of this approach has been limited by the intrinsic difficulties of the task. Controlling the interaction between the robot and the environment in a meaningful and safe way is hard especially when little prior knowledge is available. We push the idea that haptic feedback can enhance the way robots interact with unmodeled environments. We approach grasping and manipulation as tasks driven mainly by tactile and force feedback. We implemented a grasping behavior on a robotic platform with sensitive tactile sensors and compliant actuators; the behavior allows the robot to grasp objects placed on a table. Finally, we demonstrate that the haptic feedback originated by the interaction with the objects carries implicit information about their shape and can be useful for learning.

We propose that connectionism and dynamic systems theory are strong contenders for a general theory of development that holds true whatever the content domain. We illustrate, through our own career narratives, the origins of these theories in motor and language development. We situate connectionism and dynamic systems among other classic and contemporary theories and conclude that, although there are meaningful differences, these differences pale in relation to the shared assumptions about the fundamental processes and mechanisms of change.

Current models of psychological development rely heavily on connectionist models that use supervised learning. These models adapt network weights when the network output does not match the target outputs computed by some agent. The authors present a model of motor learning in which the child uses exploration to discover appropriate ways of responding. The model is consistent with what is known about how neural systems evaluate behavior. The authors model the development of reaching and investigate N. Bernstein’s (1967) hypotheses about early motor learning. Simulations show the course of learning as well as model the kinematics of reaching by a dynamical arm. Almost all developmental theories assume that a child’s interaction with the environment plays an important role in development. Often this interaction leads to long-term changes in behavior that can best be described as learning. Modern theories of learning characterize the process as exploratory, as involving the variation and selection of behavior or strategies, or as the discovery of

Abstract. In this paper, we investigate by means of statistical and information-theoretic measures, to what extent sensory-motor coordinated activity can generate and structure information in the sensory channels of a simulated agent interacting with its surrounding environment. We show how the usage of correlation, entropy, and mutual

The authors examined how visual selection mechanisms may relate to developing cognitive functions in infancy. Twenty-two 3-month-old infants were tested in 2 tasks on the same day: perceptual completion and visual search. In the perceptual completion task, infants were habituated to a partly occluded moving rod and subsequently presented with unoccluded broken and complete rod test stimuli. In the visual search task, infants viewed displays in which single targets of varying levels of salience were cast among homogeneous static vertical distractors. Infants whose posthabituation preference indicated unity perception in the completion task provided evidence of a functional visual selective attention mechanism in the search task. The authors discuss the implications of the efficiency of attentional mechanisms for information processing and learning.

Computational models have played a central role in the debate over language learnability. This article discusses how they have been used in different “stances”, from generative views to more recently introduced explanatory frameworks based on embodiment, cognitive development and cultural evolution. By digging into the details of certain specific models, we show how they organize, transform and rephrase defining questions about what makes language learning possible for children. Finally, we present a tentative synthesis to recast the debate using the notion of learning bias.

This article explores the assumption that a deeper (quantitative) understanding of the information-theoretic implications of sensory-motor coordination can help endow robots not only with better sensory morphologies, but also with better exploration strategies. Specifically, we investigate by means of statistical and informationtheoretic measures, to what extent sensory-motor coordinated activity can generate and structure information in the sensory channels of a simulated agent interacting with its surrounding environment. The results show how the usage of correlation, entropy, and mutual information can be employed (a) to segment an observed behavior into distinct behavioral states, (b) to analyze the informational relationship between the different components of the sensory-motor apparatus, and (c) to identify patterns (or fingerprints) in the sensory-motor interaction between the agent and its local environment. Key words: Self-structuring of information, sensory-motor coordination, agent-environment interaction 1

The study of how infants learn to bounce, while being supported by a harness attached to a spring, sheds light on how infants learn to exploit the dynamics of their exploratory motion. The emerging rhythmical activity -- result of an entrainment among neural system, musculo-skeletal system, and surrounding environment -- is a salient characteristic of a developing body control during the first year of life. In this paper, we describe and discuss the results of four preliminary experiments realized with a small-sized humanoid robot harnessed in a jolly jumper, and whose leg joints are controlled by neural oscillators. While the two first experiments see the robot oscillate freely in space, the last experiments have the robot touch the ground during oscillations so as to characterize the effects of ground interaction. An appropriate choice of the parameters of the neural oscillators lead to sustained and stable bouncing.