000 04062nam a22005295i 4500
001 978-3-031-01570-0
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007 cr nn 008mamaa
008 220601s2014 sz | s |||| 0|eng d
020 _a9783031015700
_9978-3-031-01570-0
024 7 _a10.1007/978-3-031-01570-0
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aChernova, Sonia.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978451
245 1 0 _aRobot Learning from Human Teachers
_h[electronic resource] /
_cby Sonia Chernova, Andrea L. Thomaz.
250 _a1st ed. 2014.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXI, 109 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
505 0 _aIntroduction -- Human Social Learning -- Modes of Interaction with a Teacher -- Learning Low-Level Motion Trajectories -- Learning High-Level Tasks -- Refining a Learned Task -- Designing and Evaluating an LfD Study -- Future Challenges and Opportunities -- Bibliography -- Authors' Biographies.
520 _aLearning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_978452
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aThomaz, Andrea L.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978453
710 2 _aSpringerLink (Online service)
_978454
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031004421
776 0 8 _iPrinted edition:
_z9783031026980
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_978455
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01570-0
912 _aZDB-2-SXSC
942 _cEBK
999 _c84593
_d84593