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Robot Learning

Jan Peters; Daniel D. Lee; Jens Kober; Duy Nguyen-Tuong; J. Andrew Bagnell; Stefan Schaal
In: Bruno Siciliano; Oussama Khatib (Hrsg.). Springer Handbook of Robotics. Pages 357-398, Springer Handbooks, Springer, 2016.


Machine learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors; conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in robot learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this chapter, we attempt to strengthen the links between the two research communities by providing a survey of work in robot learning for learning control and behavior generation in robots. We highlight both key challenges in robot learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our chapter lies on model learning for control and robot reinforcement learning. We demonstrate how machine learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

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