Titlebook: Approaches to Probabilistic Model Learning for Mobile Manipulation Robots; Jürgen Sturm Book 2013 Springer-Verlag Berlin Heidelberg 2013 A

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978-3-642-43714-4Springer-Verlag Berlin Heidelberg 2013

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Springer Tracts in Advanced Roboticshttp://image.papertrans.cn/b/image/160323.jpg

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https://doi.org/10.1007/978-3-658-02802-2eld of machine learning in general can be found in the books of Bishop (2007) and MacKay (2003). This chapter starts with a review of common machine learning techniques for regression, classification, dimensionality reduction, and clustering problems. To compare and rank alternative models, we prese

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https://doi.org/10.1007/978-3-658-02800-8ly specified by the position of its joints, and the size and orientation of its links (Craig, 1989; Sciavicco and Siciliano, 2000). Kinematic models are usually derived analytically by a robot engineer and thus rely heavily on prior knowledge about the geometry of the robot. When such a model is app

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Das Aktivierungspotential der Headline these objects are articulated such as cabinet doors and drawers, or room and garage doors. The ability to deal with such . is relevant for service robots, as, for example, they need to open doors when navigating between rooms and to open cabinets to pick up objects in fetch-and-carry applications.

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