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

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https://doi.org/10.1007/978-3-658-02800-8tion nor costly calibration equipment. This capability does not only facilitate the deployment and calibration of new robotic systems but also enables robots to autonomously adapt their models when the kinematics change, for example, as a result of hardware failures or material fatigue. Furthermore,

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1610-7438 nt in which the robot is operating, (3) using tactile feedback to augment the visual perception, and (4) learning novel manipulation tasks from human demonstrations..This book is an ideal resource for postgradu978-3-642-43714-4978-3-642-37160-8Series ISSN 1610-7438 Series E-ISSN 1610-742X

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Book 2013ing actuation and visual feedback, (2) learning about articulated objects in the environment in which the robot is operating, (3) using tactile feedback to augment the visual perception, and (4) learning novel manipulation tasks from human demonstrations..This book is an ideal resource for postgradu

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Introduction,t impact on our economy and society in the future. Mobile manipulation robots that are equipped with one or more gripper arms could fulfill various useful services in private homes such as cleaning, tidying up, or cooking, which would mean a significant time benefit to their owners. By supporting el

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Basics,eld 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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Vision-Based Perception of Articulated Objects,As input, our framework requires a sequence of pose observations of the articulated object. We implemented the perception in the previous chapter using visual markers or by directly recording the end effector trajectory while the robot was manipulating the articulated object. For the daily use in do

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Object Recognition Using Tactile Sensors,example, a robot that picks up an object from a box cannot see which object it grasps because the robot partially occludes its view with its own gripper. In particular for robotic manipulation tasks, tactile sensing provides another sensor modality that can reveal relevant aspects about the object b

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