Mastering the sensorimotor capabilities of our body is a skill that we acquire and refine over time, starting at the prenatal stages of development. This learning process is linked to brain development and is shaped by the rich set of multimodal information experienced while exploring and interacting with the environment. Evidence coming from neuroscience suggests the brain forms and mantains body representations as the main strategy to this mastering. Although it is still not clear how this knowledge is represented in our brain, it is reasonable to think that such internal models of the body undergo a continuous process of adaptation. They need to match growing corporal dimensions during development, as well as temporary changes in the characteristics of the body, such as the transient morphological alterations produced by the usage of tools. In the robotics community there is an increasing interest in reproducing similar mechanisms in artificial agents, mainly motivated by the aim of producing autonomous adaptive systems that can deal with complexity and uncertainty in human environments. Although promising results have been achieved in the context of sensorimotor learning and autonomous generation of body representations, it is still not clear how such low-level representations can be scaled up to more complex motor skills and how they can enable the development of cognitive capabilities. Recent findings from behavioural and brain studies suggests that processes of mental simulations of action-perception loops are likely to be executed in our brain and are dependent on internal motor representations. The capability to simulate sensorimotor experience might represent a key mechanism behind the implementation of further cognitive skills, such as self-detection, self-other distinction and imitation. Empirical investigation on the functioning of similar processes in the brain and on their implementation in artificial agents is fragmented. This e-book comprises a collection of manuscripts published by Frontiers in Robotics and Artificial Intelligence, under the section Humanoid Robotics, on the research topic re-enactment of sensorimotor experience for cognition in artificial agents. This compendium aims at condensing the latest theoretical, review and experimental studies that address new paradigms for learning and integrating multimodal sensorimotor information in artificial agents, re-use of the sensorimotor experience for cognitive development and further construction of more complex strategies and behaviours using these concepts. The authors would like to thank M.A. Dylan Andrade for his art work for the cover.

This project applies principles from the field of Computational Neuroscience to Robotics research, in particular to develop systems inspired by how nature manages to solve sensorimotor coordination tasks. The overall aim has been to build a self-organising sensorimotor system using biologically inspired techniques based upon human cortical development which can in the future be implemented in neuromorphic hardware. This can then deliver the benefits of low power consumption and real time operation but with flexible learning onboard autonomous robots. A core principle is the Self-Organising Feature Map which is based upon the theory of how 2D maps develop in real cortex to represent complex information from the environment. A framework for developing feature maps for both motor and visual directional selectivity representing eight different directions of motion is described as well as how they can be coupled together to make a basic visuomotor system. In contrast to many previous works which use artificially generated visual inputs (for example, image sequences of oriented moving bars or mathematically generated Gaussian bars) a novel feature of the current work is that the visual input is generated by a DVS 128 silicon retina camera which is a neuromorphic device and produces spike events in a frame-free way. One of the main contributions of this work has been to develop a method of autonomous regulation of the map development process which adapts the learning dependent upon input activity. The main results show that distinct directionally selective maps for both the motor and visual modalities are produced under a range of experimental scenarios. The adaptive learning process successfully controls the rate of learning in both motor and visual map development and is used to indicate when sufficient patterns have been presented, thus avoiding the need to define in advance the quantity and range of training data. The coupling training experiments show that the visual input learns to modulate the original motor map response, creating a new visual-motor topological map.

The Springer Handbook for Computational Intelligence is the first book covering the basics, the state-of-the-art and important applications of the dynamic and rapidly expanding discipline of computational intelligence. This comprehensive handbook makes readers familiar with a broad spectrum of approaches to solve various problems in science and technology. Possible approaches include, for example, those being inspired by biology, living organisms and animate systems. Content is organized in seven parts: foundations; fuzzy logic; rough sets; evolutionary computation; neural networks; swarm intelligence and hybrid computational intelligence systems. Each Part is supervised by its own Part Editor(s) so that high-quality content as well as completeness are assured.

A comprehensive overview of an interdisciplinary approach to robotics that takes direct inspiration from the developmental and learning phenomena observed in children's cognitive development. Developmental robotics is a collaborative and interdisciplinary approach to robotics that is directly inspired by the developmental principles and mechanisms observed in children's cognitive development. It builds on the idea that the robot, using a set of intrinsic developmental principles regulating the real-time interaction of its body, brain, and environment, can autonomously acquire an increasingly complex set of sensorimotor and mental capabilities. This volume, drawing on insights from psychology, computer science, linguistics, neuroscience, and robotics, offers the first comprehensive overview of a rapidly growing field. After providing some essential background information on robotics and developmental psychology, the book looks in detail at how developmental robotics models and experiments have attempted to realize a range of behavioral and cognitive capabilities. The examples in these chapters were chosen because of their direct correspondence with specific issues in child psychology research; each chapter begins with a concise and accessible overview of relevant empirical and theoretical findings in developmental psychology. The chapters cover intrinsic motivation and curiosity; motor development, examining both manipulation and locomotion; perceptual development, including face recognition and perception of space; social learning, emphasizing such phenomena as joint attention and cooperation; language, from phonetic babbling to syntactic processing; and abstract knowledge, including models of number learning and reasoning strategies. Boxed text offers technical and methodological details for both psychology and robotics experiments.