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001 978-3-642-37160-8
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008 130508s2013 gw | s |||| 0|eng d
020 _a9783642371608
_9978-3-642-37160-8
024 7 _a10.1007/978-3-642-37160-8
_2doi
050 4 _aTJ210.2-211.495
050 4 _aT59.5
072 7 _aTJFM1
_2bicssc
072 7 _aTEC037000
_2bisacsh
072 7 _aTEC004000
_2bisacsh
082 0 4 _a629.892
_223
100 1 _aSturm, J�urgen.
_eauthor.
245 1 0 _aApproaches to Probabilistic Model Learning for Mobile Manipulation Robots
_h[electronic resource] /
_cby J�urgen Sturm.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXXV, 204 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Tracts in Advanced Robotics,
_x1610-7438 ;
_v89
505 0 _aIntroduction -- Basics -- Body Schema Learning -- Learning Kinematic Models of Articulated Objects -- Vision-based Perception of Articulated Objects -- Object Recognition using Tactile Sensors -- Object State Estimation using Tactile Sensors -- Learning Manipulation Tasks by Demonstration -- Conclusions.
520 _aMobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context. Examples include domestic service robots that implement large parts of the housework, and versatile industrial assistants that provide automation, transportation, inspection, and monitoring services. The challenge in these applications is that the robots have to function under changing, real-world conditions, be able to deal with considerable amounts of noise and uncertainty, and operate without the supervision of an expert. This book presents novel learning techniques that enable mobile manipulation robots, i.e., mobile platforms with one or more robotic manipulators, to autonomously adapt to new or changing situations. The approaches presented in this book cover the following topics: (1) learning the robot's kinematic structure and properties using 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 postgraduates and researchers working in robotics, computer vision, and artificial intelligence who want to get an overview on one of the following subjects: �         kinematic modeling and learning, �         self-calibration and life-long adaptation, �         tactile sensing and tactile object recognition, and �         imitation learning and programming by demonstration.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aImage processing.
650 0 _aRobotics.
650 0 _aAutomation.
650 1 4 _aEngineering.
650 2 4 _aRobotics and Automation.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aImage Processing and Computer Vision.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642371592
830 0 _aSpringer Tracts in Advanced Robotics,
_x1610-7438 ;
_v89
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-37160-8
912 _aZDB-2-ENG
942 _cEBK
999 _c57720
_d57720