Abstract
To accomplish a particular manipulation task, a robot needs a detailed description of how to execute it. However, it is not possible to specify all potential tasks of a manipulation robot beforehand. For example, robotic assistants operating in industrial contexts are frequently faced with changes in the production process. As a consequence, novel manipulation skills become relevant on a regular basis. For this reason, there is a need for solutions that enable normal users to quickly and intuitively teach new manipulation skills to a robot.
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© 2013 Springer-Verlag Berlin Heidelberg
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Sturm, J. (2013). Learning Manipulation Tasks by Demonstration. In: Approaches to Probabilistic Model Learning for Mobile Manipulation Robots. Springer Tracts in Advanced Robotics, vol 89. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37160-8_8
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DOI: https://doi.org/10.1007/978-3-642-37160-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37159-2
Online ISBN: 978-3-642-37160-8
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