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Titlebook: Analysis and Design of Machine Learning Techniques; Evolutionary Solutio Patrick Stalph Book 2014 Springer Fachmedien Wiesbaden 2014 Human

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發(fā)表于 2025-3-21 19:59:30 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Analysis and Design of Machine Learning Techniques
期刊簡稱Evolutionary Solutio
影響因子2023Patrick Stalph
視頻videohttp://file.papertrans.cn/157/156191/156191.mp4
發(fā)行地址Publication in the field of technical sciences.Includes supplementary material:
圖書封面Titlebook: Analysis and Design of Machine Learning Techniques; Evolutionary Solutio Patrick Stalph Book 2014 Springer Fachmedien Wiesbaden 2014 Human
影響因子Manipulating or grasping objects seems like a trivial task for humans, as these are motor skills of everyday life. Nevertheless, 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 explanations for human learning. The fundamental challenge, that motivates Patrick Stalph, originates from the cognitive science: How do humans learn their motor skills? The author makes a connection between robotics and cognitive sciences by analyzing motor skill 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 are plausible from a cognitive viewpoint and feasible for the roboticist. The power and scalability of those algorithms is evaluated in theoretical 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.
Pindex Book 2014
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Basics of Kinematic Robot Controlectional control tasks such as reaching for objects. Learning is realized by algorithms that mimic brain function at least to some degree. Therefore the framework developed herein . explain how the brain learns motor control. Of course, there is no proof because a concrete implementation in one or t
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Visual Servoing for the iCub process. The present chapter introduces a more realistic scenario, where a physics engine complements the simulation and the end effector location is not simply ., but is . by means of stereo cameras. This is also called visual servoing [21], where vision is used for closed loop control.
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https://doi.org/10.1007/978-3-662-26253-5ings. A multitude of algorithm classes are introduced, including simple model fitting, interpolation, and advanced concepts such as Gaussian Processes and Artificial Neural Networks. The last section of this chapter discusses the applicability, but also questions the plausibility of such algorithms in the light of brain functionality.
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