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This paper introduces a method for the coordination of individual action within a group of robots that have to accomplish a common task, gathering energy in a dynamic environment and transferring this energy to a nest. Each individual behavioral pattern is driven by an internal neural rhythm generator exhibiting quasi-periodic oscillations. The paper(More)
The artificial life approach to evolutionary robotics is used as a fundamental framework for the development of a modular neural control of autonomous mobile robots. The applied evolutionary technique is especially designed to grow different neural structures with complex dynamical properties. This is due to a modular neurodynamics approach to cognitive(More)
—Understanding the mechanism mediating the change from inaccurate pre-reaching to accurate reaching in infants may confer advantage from both a robotic and biological research perspective. In this work, we present a biologically meaningful learning scheme applied to the coordination between reach and gaze within a robotic structure. The system is model-free(More)
A modular approach to neural behavior control of autonomous robots is presented. It is based on the assumption that complex internal dynamics of recurrent neural networks can efficiently solve complex behavior tasks. For the development of appropriate neural control structures an evolutionary algorithm is introduced, which is able to generate neuromodules(More)
This case study demonstrates how the synthesis and the analysis of minimal recurrent neural robot control provide insights into the exploration of embodiment. By using structural evolution, minimal recurrent neural networks of general type were evolved for behavior control. The small size of the neural structures facilitates thorough investigations of(More)
—Inspired by child development and brain research, we introduce a computational framework which integrates robotic active vision and reaching. Essential elements of this framework are sensorimotor mappings that link three different computational domains relating to visual data, gaze control, and reaching. The domain of gaze control is the central(More)
To study the relevance of recurrent neural network structures for the behavior of autonomous agents a series of experiments with miniature robots is performed. A special evolutionary algorithm is used to generate networks of diierent sizes and architectures. Solutions for obstacle avoidance and phototropic behavior are presented. Networks are evolved with(More)
Early infancy is a time of remarkable sensorimotor learning and rapid cognitive growth. Such development offers a rich source of inspiration for models that might allow robotic systems to learn cumulatively and autonomously. This article consists of three parts. The first part introduces the key issues from a robotics perspective. In particular , we promote(More)
In this paper the role of non-linear control structures for the development of multifunctional robot behavior in a self-organized way is discussed. This discussion is based on experiments where combinations of two behavioral tasks are incrementally evolved. The evolutionary experiments develop recurrent neural networks of general type in a systematically(More)