000			03885nam a22004695i 4500
001			978-3-642-05181-4
003			DE-He213
005			20140220084527.0
007			cr nn 008mamaa
008			100301s2010 gw | s |||| 0|eng d
020			_a9783642051814 _9978-3-642-05181-4
024	7		_a10.1007/978-3-642-05181-4 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aSigaud, Olivier. _eeditor.
245	1	0	_aFrom Motor Learning to Interaction Learning in Robots _h[electronic resource] / _cedited by Olivier Sigaud, Jan Peters.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010.
300			_bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Computational Intelligence, _x1860-949X ; _v264
505	0		_aFrom Motor Learning to Interaction Learning in Robots -- From Motor Learning to Interaction Learning in Robots -- I: Biologically Inspired Models for Motor Learning -- Distributed Adaptive Control: A Proposal on the Neuronal Organization of Adaptive Goal Oriented Behavior -- Proprioception and Imitation: On the Road to Agent Individuation -- Adaptive Optimal Feedback Control with Learned Internal Dynamics Models -- The SURE_REACH Model for Motor Learning and Control of a Redundant Arm: From Modeling Human Behavior to Applications in Robotics -- Intrinsically Motivated Exploration for Developmental and Active Sensorimotor Learning -- II: Learning Policies for Motor Control -- Learning to Exploit Proximal Force Sensing: A Comparison Approach -- Learning Forward Models for the Operational Space Control of Redundant Robots -- Real-Time Local GP Model Learning -- Imitation and Reinforcement Learning for Motor Primitives with Perceptual Coupling -- A Bayesian View on Motor Control and Planning -- Methods for Learning Control Policies from Variable-Constraint Demonstrations -- Motor Learning at Intermediate Reynolds Number: Experiments with Policy Gradient on the Flapping Flight of a Rigid Wing -- III: Imitation and Interaction Learning -- Abstraction Levels for Robotic Imitation: Overview and Computational Approaches -- Learning to Imitate Human Actions through Eigenposes -- Incremental Learning of Full Body Motion Primitives -- Can We Learn Finite State Machine Robot Controllers from Interactive Demonstration? -- Mobile Robot Motion Control from Demonstration and Corrective Feedback -- Learning Continuous Grasp Affordances by Sensorimotor Exploration -- Multimodal Language Acquisition Based on Motor Learning and Interaction -- Human-Robot Cooperation Based on Interaction Learning.
520			_aFrom an engineering standpoint, the increasing complexity of robotic systems and the increasing demand for more autonomously learning robots, has become essential. This book is largely based on the successful workshop “From motor to interaction learning in robots” held at the IEEE/RSJ International Conference on Intelligent Robot Systems. The major aim of the book is to give students interested the topics described above a chance to get started faster and researchers a helpful compandium.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aRobotics and Automation.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aPeters, Jan. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642051807
830		0	_aStudies in Computational Intelligence, _x1860-949X ; _v264
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-05181-4
912			_aZDB-2-ENG
999			_c111688 _d111688