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 aim of the present paper is to propose that the adoption of a framework of biological development is suitable for the construction of artificial systems. We will argue that a developmental approach does provide unique insights on how to build highly complex and adaptable artificial systems. To illustrate our point, we will use as an example the acquisition of goal-directed reaching. In the initial part of the paper we will outline a) how mechanisms of biological development can be adapted to the artificial world, and b) how this artificial development differs from traditional engineering approaches to robotics. An experiment performed on an artificial system initially controlled by motor reflexes is presented, showing the acquisition of visuo-motor maps for ballistic control of reaching without explicit knowledge of the system's kinematic parameters.

This article describes a developmental system based on information theory implemented on a real robot that learns a model of its own sensory and actuator apparatus. There is no innate knowledge regarding the modalities or representation of the sensory 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 basic visually guided movement.

Learning through imitation is a powerful and versatile method for acquiring new behaviors. In humans, a wide range of behaviors, from styles of social interaction to tool use, are passed from one generation to another through imitative learning. Although imitation evolved through Darwinian means, it achieves Lamarckian ends: it is a mechanism for the inheritance of acquired characteristics. Unlike trial-and-error-based learning methods such as reinforcement learning, imitation allows rapid learning.

Given a neural control structure, what would be the impact of body growth on control performance? This question, which ad- dresses the issue of the interaction between in- nate structure, ongoing developing structure and experience, is very relevant to the field of epigenetic robotics. Much of the early social interaction is done as the body develops and the interplay cannot be ignored. We hypothesize that starting with fewer degrees of freedom enables a more efficient exploration of the sensorimotor space, that results in multiple directions of stability. While not necessarily corresponding to optimal task performance, they will guide the coordination of additional degrees of freedom. These additional degrees of freedom then allow for optimal task per- formance as well as for more tolerance and adaptation to environmental interaction. We propose a simple case-study to validate our hypothesis and describe experiments with a small humanoid robot.

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Developmental robotics is an emerging field located at the intersection of developmental psychology and robotics, that has lately attracted quite some attention. This paper gives a survey of a variety of research projects dealing with or inspired by developmental issues, and outlines possible future directions.

Starting with fewer degrees of freedom has been shown to enable a more efficient exploration of the sensorimotor space.

A prerequisite for autonomous acquisition of novel behaviors would be the ability to evolve internal representation of embodied interaction structures, in other words, sensory-motor categorization. It requires a unified approach to categorical learning of spatiotemporal information. This paper proposes a novel artificial neural architecture for such processing. It is based on a `spiking neuron' model and a temporal learning rule, and is capable of spatio-temporal association and categorization. The architecture has been applied to an example of visuo-ocular interaction with its surrounding by a binocular active vision system. Its sensory-motor interactions are triggered by a set of a priori reflexes and the neural architecture successfully categorized resulting structure of the interaction. Results from simulation experiments are presented. 1. Introduction To understand the dynamics of a system engaged in interaction with its surrounding, the observation and categorization of its expl...

The paper examines characteristics of interactive learning between human tutors and a robot having a dynamic neural network model which is inspired by human parietal cortex functions. A humanoid robot, with a recurrent neural network that has a hierarchical structure, learns to manipulate objects. Robots learn tasks in repeated self-trials with the assistance of human interaction which provides physical guidance until tasks are mastered and learning is consolidated within neural networks. Experimental results and the analyses showed that 1) codevelopmental shaping of task behaviors stems from interactions between the robot and tutor, 2) dynamic structures for articulating and sequencing of behavior primitives are selforganized in the hierarchically organized network, and 3) such structures can afford both generalization and context-dependency in generating skilled behaviors.