Artificial Neural Systems for Robots

Artificial Neural Systems for Robots

Phil Husbands (Centre for Computational Neuroscience and Robotics, UK), Andy Philippides (Centre for Computational Neuroscience and Robotics, UK) and Anil K. Seth (Centre for Computational Neuroscience and Robotics, UK & University of Sussex, UK)
DOI: 10.4018/978-1-60960-021-1.ch010
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This chapter reviews the use of neural systems in robotics, with particular emphasis on strongly biologically inspired neural networks and methods. As well as describing work at the research frontiers, the paper provides some historical background in order to clarify the motivations and scope of work in this field. There are two major sections that make up the bulk of the chapter: one surveying the application of artificial neural systems to robot control, and one describing the use of robots as tools in neuroscience. The former concentrates on biologically derived neural architectures and methods used to drive robot behaviours, and the latter introduces a closely related area of research where robotic models are used as tools to study neural mechanisms underlying the generation of adaptive behaviour in animals and humans.
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Despite the construction of many ingenious mechanical automata over the centuries (including chess playing Turks and flatulent ducks (Wood 2003)), it was not until the 1930s that devices recognizable as robots (in the present day sense of the term) appeared. Early mobile robots, such as Thomas Ross’s ‘robot rat’, completed in 1935, were designed for narrowly focused single behaviours (often maze running) and employed highly specific mechanisms to achieve their intended task (Cordeschi, 2002). These ‘artificial animals’ inspired what were probably the very first examples of more general mobile autonomous robots – W. Grey Walter’s tortoises (Walter, 1950). These robots were also the first to employ an early form of neural network as their artificial nervous system. They were born out of the cybernetics movement, a highly interdisciplinary endeavour – drawing together pioneers of computing and modern neuroscience – which was the forerunner of much of contemporary AI and robotics, and the origin of artificial neural networks and evolutionary computing, as well as control and information theory (Boden, 2006; Husbands et al., 2008).

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