Robot Learning

Peters, J; Tedrake, R; Roy, N; Morimoto, J

doi:10.1007/978-0-387-30164-8_732

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Robot Learning

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Peters, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://link.springer.com/content/pdf/10.1007%2F978-0-387-30164-8_732.pdf
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Citation

Peters, J., Tedrake, R., Roy, N., & Morimoto, J. (2011). Robot Learning. In C. Sammut, & G. Webb (Eds.), Encyclopedia of Machine Learning (2010 edition, pp. 865-869). New York, NY, USA: Springer.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BCE4-2

Abstract

Robot learning consists of a multitude of machine learning approaches, particularly reinforcement learning, inverse reinforcement learning and regression methods. These methods have been adapted sufficiently to domain to achieve real-time learning in complex robot systems such as helicopters, flapping-wing flight, legged robots, anthropomorphic arms, and humanoid robots.