Abstract
In this book, we presented several innovative techniques that enable mobile manipulation robots to robustly operate in unstructured environments under changing, real-world conditions, which is essential for the success of mobile manipulation robots in the future. Many of the relevant applications require that robots function robustly in new situations while they are dealing with considerable amounts of noise and uncertainty. Therefore, the main objective of this work was to develop novel approaches that enable manipulation robots to autonomously acquire the models they need to successfully implement their service tasks.
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© 2013 Springer-Verlag Berlin Heidelberg
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Sturm, J. (2013). Conclusions. 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_9
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DOI: https://doi.org/10.1007/978-3-642-37160-8_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37159-2
Online ISBN: 978-3-642-37160-8
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