Analysis and design of machine learning

Analysis and Design of Machine learning Techniques Manipulating or graspi ng objects seems like a trivial task for humans, as these are motor skills of everyday lift, :\'evertheless, motor skills are not easy to learn for humans and this is also an active research topic in robotics However, most solutions are optimized for industrial applications and, thus, few are plausible nplanations for human learning The fundamental challenge, that motivates Patrick Stalph, originates from the cognitive science: How humans learn their motor skills? The author makes a connection between robotics and cognitive sciences by analyzing motor ski II learning using implementations that could be found in the human brain - at least to some extent Therefore three suitable machine learning algorithms are selected - algorithms that arc plausible from a cognitive viewpoint and feasible for the roboticist The power and scalability of those algorithms is evaluated in theon:tical simulations and more realistic scenarios with the iCub humanoid robot Convincing results confirm the applicability of the approach, while the biological plausibility is discussed in retrospect Contents • How humans learn their motor skills? • Evolutionary machine learning algorithms • Application to simulated robots Target Groups • Researchers interested in artificial intelligence, cognitive sciences or robotics • Roboticists interested in integrating machine learning About the Author Patrick Stalph was a Ph.D student at the chair of Cognitive Modeling, which is led by Prof Butz at the University of Tlibingen ~ springer-vieweg.de ~ - ~ - -