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Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor


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CiteScore 2017: 0.33
SCImago Journal Rank (SJR) 2017: 0.104
ICV 2017: 99.90



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2081-4836
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Learning Object Relationships which determine the Outcome of Actions

Severin Fichtl / John Alexander / Dirk Kraft / Jimmy Alison Jørgensen / Norbert Krüger / Frank Guerin
Published Online: 2013-04-15 | DOI: https://doi.org/10.2478/s13230-013-0104-x

Abstract

Infants extend their repertoire of behaviours from initially simple behaviours with single objects to complex behaviours dealing with spatial relationships among objects. We are interested in the mechanisms underlying this development in order to achieve similar development in artificial systems. One mechanism is sensorimotor differentiation, which allows one behaviour to become altered in order to achieve a different result; the old behaviour is not forgotten, so differentiation increases the number of available behaviours. Differentiation requires the learning of both sensory abstractions and motor programs for the new behaviour; here we focus only on the sensory aspect: learning to recognise situations in which the new behaviour succeeds. We experimented with learning these situations in a realistic physical simulation of a robotic manipulator interacting with various objects, where the sensor space includes the robot arm position data and a Kinect-based vision system. The mechanism for learning sensory abstractions for a new behaviour is a component in the larger enterprise of building systems which emulate the mechanisms of infant development.

Keywords: Developmental Artificial Intelligence; Vision; Infant Development; Means-end Behaviour; Learning Preconditions

References

  • [1] Frank Guerin, Dirk Kraft, and Norbert Krüger. A survey of the ontogeny of tool use: from sensorimotor experience to planning. IEEE Transactions on Autonomous Mental Development, 5(1):18–45, 2013.Google Scholar

  • [2] J. Piaget. The Origins of Intelligence in Children. London: Routledge & Kegan Paul, 1936. (French version 1936, translation 1952).Google Scholar

  • [3] Jeffrey J. Lockman. A perception-action perspective on tool use development. Child Development, 71(1):137–144, 2000.Google Scholar

  • [4] L. B. Smith. Dynamic systems, sensori-motor processes and the origins of stability and flexibility. In J. Spencer, M. Thomas, and J. McClelland, editors, Toward a unified theory of development: Connectionism and dynamic systems theories reconsidered. Ox. Uni. Press, 2009.Google Scholar

  • [5] Kurt W. Fischer. A theory of cognitive development: The control and construction of hierarchies of skills. Psychological Review, 87(6):477–531, 1980.Google Scholar

  • [6] P. Willatts. Development of problem-solving strategies in infancy. In D.F. Bjorklund, editor, Children’s Strategies: Contemporary Views of Cognitive Development, pages 23–66. Lawrence Erlbaum, 1990.Google Scholar

  • [7] J. Piaget. The Construction of Reality in the Child. London: Routledge & Kegan Paul, 1937. (French version 1937, translation 1955).Google Scholar

  • [8] Benjamin Rosman and Subramanian Ramamoorthy. Learning spatial relationships between objects. The International Journal of Robotics Research, 30(11):1328–1342, 2011.Google Scholar

  • [9] Richard S. Sutton. Verification, the key to ai, 2006. Unpublished document, available on author’s webpage http://www.cs.ualberta.ca/~sutton/IncIdeas/KeytoAI.html.

  • [10] A. Stoytchev. Some basic principles of developmental robotics. IEEE Trans. Auton. Mental Development, 1(2):122–130, 2009.Web of ScienceGoogle Scholar

  • [11] Esther Thelen. Rhythmical stereotypies in normal human infants. Animal Behaviour, 27(3):699–715, Dec 1979.CrossrefGoogle Scholar

  • [12] T. G. R. Bower. Development in Infancy. San Francisco : W.H. Freeman, 1982.Google Scholar

  • [13] Kristine S. Bourgeois, Alexa W. Khawar, S. Ashley Neal, and Jeffrey J. Lockman. Infant manual exploration of objects, surfaces, and their interrelations. Infancy, 8(3):233–252, 2005.Google Scholar

  • [14] Jean Piaget. The Child’s Conception of the World. Harcourt, Brace and Co., New York, 1929.Google Scholar

  • [15] Harold Chaput. The Constructivist Learning Architecture: A Model of Cognitive Development for Robust Autonomous Robots. PhD Thesis, (The University of Texas at Austin, Artificial Intelligence Laboratory), 2004.Google Scholar

  • [16] Severin Fichtl. Making Artificial Intelligence which Copies the Way Babies Learn. Master Thesis, (Aberdeen University, Department of Computing Science), 2011.Google Scholar

  • [17] M Rolf, J J Steil, and M Gienger. Online Goal Babbling for rapid bootstrapping of inverse models in high dimensions. In Development and Learning (ICDL), 2011 IEEE International Conference on, volume 2, pages 1–8, 2011.Google Scholar

  • [18] Gert Kootstra, Mila Popović, Jimmy Alison Jøstate spacergensen, Kamil Kuklinski, Konstantsin Miatliuk, Danica Kragic, and Norbert Krüger. Enabling grasping of unknown objects through a synergistic use of edge and surface information. Int. J. Rob. Res., 31(10):1190–1213, September 2012.Google Scholar

  • [19] J. A. Jørgensen, L.-P. Ellekilde, and H. G. Petersen. RobWorkSim - an Open Simulator for Sensor based Grasping. In ISR/ROBOTIK 2010 (41 st International Symposium). VDE-Verlag, June 2010.Google Scholar

  • [20] SørenMaagaard Olesen, Simon Lyder, Dirk Kraft, Norbert Krüger, and JeppeBarsøe Jessen. Real-time extraction of surface patches with associated uncertainties by means of Kinect cameras. Journal of Real-Time Image Processing, pages 1–14, 2012.Google Scholar

  • [21] Kourosh Khoshelham and Sander Oude Elberink. Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications. Sensors, 12(2):1437–1454, 2012.Google Scholar

  • [22] R B Rusu and S Cousins. 3D is here: Point Cloud Library (PCL). In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages 1–4, May 2011.Google Scholar

  • [23] N. Pugeault, F. Wörgötter, and N. Krüger. Visual primitives: Local, condensed, and semantically rich visual descriptors and their applications in robotics. International Journal of Humanoid Robotics (Special Issue on Cognitive Humanoid Vision), 7(3):379–405, 2010.Web of ScienceCrossrefGoogle Scholar

  • [24] Martin Riedmiller and Heinrich Braun. RPROP - A Fast Adaptive Learning Algorithm. In Proceedings of the International Symposium on Computer and Information Science VII, Institut fur Logik, Komplexitat und Deduktionssysteme, Universitat Karlsruhe, 1992.Google Scholar

  • [25] M Riedmiller and H Braun. A direct adaptive method for faster backpropagation learning: the RPROP algorithm. IEEE International Conference on Neural Networks, 1993.Google Scholar

  • [26] Miroslav Kubat, Robert Holte, and Stan Matwin. Learning when negative examples abound. In Proceedings of the 9th European Conference on Machine Learning, ECML ’97, pages 146–153, London, UK, UK, 1997. Springer-Verlag.Google Scholar

  • [27] Severin Fichtl, John Alexander, Dirk Kraft, Jimmy Alison Jorgensen, Norbert Krüger, and Frank Guerin. Rapidly learning preconditions for means-ends behaviour using active learning. In 2nd IEEE International Conference on Development and Learning and Epigenetic Robotics, 2012.Google Scholar

  • [28] Matti Kääriäinen. Active Learning in the Non-realizable Case. In José L. Balcázar, Philip M. Long, and Frank Stephan, editors, Algorithmic Learning Theory, volume 4264 of Lecture Notes in Computer Science, pages 63–77. Springer Berlin Heidelberg, 2006.Google Scholar

  • [29] Donald E Knuth. Big Omicron and big Omega and big Theta. SIGACT News, 8(2):18–24, April 1976.Google Scholar

  • [30] Sara Stolbach. Active Learning Models and Noise. Course Paper, Columbia University, (Advanced Topics in Computational Learning Theory, COMS 6253), 2007.Google Scholar

  • [31] Ina C. Uzgiris and J. McV. Hunt. Assessment in infancy : ordinal scales of psychological development. University of Illinois Press, 1975.Google Scholar

  • [32] E. Bates, V. Carlson-Luden, and I. Bretherton. Perceptual aspects of tool-using in infancy. Infant Behavior and Development, 3(2):181–190, 1980.Google Scholar

  • [33] Matthew Schlesinger and Jonas Langer. Infants’ developing expectations of possible and impossible tool-use events between ages 8 and 12 months. DevelopmentalScience, 2(2):195–205, 1999.Google Scholar

  • [34] P.J. Kellman and M.E. Arterberry. The Cradle of Knowledge. MIT-Press, 1998.Google Scholar

  • [35] J. M. Mandler. How to build a baby: II. conceptual primitives. Psychological Review, 99(4):587–604, 1992.PubMedCrossrefGoogle Scholar

  • [36] Denis Mareschal. Computational perspectives on cognitive development. Wiley Interdisciplinary Reviews: Cognitive Science, 1(5):696–708, 2010.Google Scholar

  • [37] Paolo Tommasino, Daniele Caligiore, Marco Mirolli, and Gianluca Baldassarre. Reinforcement learning algorithms that assimilate and accommodate behaviours: an experimental comparison. In 2nd IEEE International Conference on Development and Learning and Epigenetic Robotics, 2012.Google Scholar

  • [38] A.G.E. Collins and M.J. Frank. Cognitive control over learning: Creating, clustering and generalizing task-set structure. Psychological Review, 120:190–229, 2013.Web of ScienceGoogle Scholar

  • [39] Terry Zimmerman and Subbarao Kambhampati. Learning-assisted automated planning: Looking back, taking stock, going forward. AI MAGAZINE, 24(2):73–96, 2003.Google Scholar

  • [40] J. Mugan and B. Kuipers. Autonomous learning of high-level states and actions in continuous environments. IEEE Trans. Autonomous Mental Development, 4(1):70–86, 2012.Web of ScienceGoogle Scholar

  • [41] Kira Mourão, Ronald Petrick, and Mark Steedman. Using kernel perceptrons to learn action effects for planning. International Conference on Cognitive Systems (CogSys 2008), pages 45–50, 2008.Google Scholar

  • [42] E. Ugur, E. Oztop, and E. Sahin. Goal emulation and planning in perceptual space using learned affordances, 2011.Web of ScienceGoogle Scholar

  • [43] A. Ferry, S.J. Hespos, and S. Waxman. Language facilitates category formation in 3-month-old infants. Child Development, 81(2):472–479, 2010.Google Scholar

  • [44] S. J. Hespos and T. Piccin. To generalize or not to generalize: Spatial categories are influenced by physical attributes and language. Developmental Science, 12(1):88–95, 2009.PubMedCrossrefWeb of ScienceGoogle Scholar

  • [45] Marianella Casasola. Can language do the driving? the effect of linguistic input on infants’ categorization of support spatial relations. Developmental Psychology, 41(1):183–192, 2005.CrossrefGoogle Scholar

  • [46] Katharina J Rohlfing. Facilitating the acquisition of under by means of in and on–a training study in polish. J Child Lang, 33(1):51–69, 2006.Google Scholar

  • [47] Jean Matter Mandler. The foundations of conceptual thought in infancy. Cognitive Development, 7(3):273–285, 1992.Google Scholar

About the article

Received: 2012-12-15

Accepted: 2013-03-27

Published Online: 2013-04-15

Published in Print: 2012-12-01


Citation Information: Paladyn, Journal of Behavioral Robotics, Volume 3, Issue 4, Pages 188–199, ISSN (Online) 2081-4836, DOI: https://doi.org/10.2478/s13230-013-0104-x.

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© Severin Fichtl et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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