ObjectAction Complexes: Grounded abstractions of sensorymotor processes

Robotics and Autonomous Systems

Volumn 59, Issue 10, 2011, Pages 740-757

  Krüger, Norbert ^a   Geib, Christopher ^b   Piater, Justus ^c   Petrick, Ronald ^b   Steedman, Mark ^b   Wörgötter, Florentin ^d   Ude, Aleš ^e   Asfour, Tamim ^f   Kraft, Dirk ^a   Omrčen, Damir ^e   Agostini, Alejandro ^g   Dillmann, Rdiger ^f  

^a UNIVERSITY OF SOUTHERN DENMARK   (Denmark)

^b UNIVERSITY OF EDINBURGH   (United Kingdom)

^c UNIVERSITY OF INNSBRUCK   (Austria)

^d Bernstein Center for Computational Neuroscience (BCCN)   (Germany)

^e JO EF STEFAN INSTITUTE   (Slovenia)

^f KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

^g INSTITUT DE ROBÒTICA I INFORMÀTICA INDUSTRIAL CSIC UPC   (Spain)

Author keywords

Execution monitoring; Grounding; Machine learning; Reasoning about action and change; Robotics

Indexed keywords

COGNITIVE ROBOTS; EXECUTION MONITORING; FORMAL DEFINITION; LEARNING PROBLEM; MACHINE-LEARNING; REASONING ABOUT ACTIONS; SYMBOLIC REPRESENTATION;

COGNITIVE SYSTEMS;

COMPLEXATION;

EID: 80051824498     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.robot.2011.05.009     Document Type: Article

References (59)

1. R.A. Brooks A robust layered control system for a mobile robot IEEE Journal of Robotics and Automation 2 1986 14 23
2. R.A. Brooks, C. Breazeal, M. Marjanovic, B. Scassellati, and M.M. Williamson The Cog project: building a humanoid robot Lecture Notes in Computer Science 1562 1999 52 87
3. V. Braitenberg Vehicles: Experiments in Synthetic Psychology 1986 The MIT Press
4. M. Huber, A hybrid architecture for adaptive robot control, Ph.D. Thesis, University of Massachusetts Amherst, 2000.
5. A. Stoytchev Some basic principles of developmental robotics IEEE Transactions on Autonomous Mental Development 1 2 2009 1 9
6. J. Modayil, B. Kuipers, Bootstrap learning for object discovery, in: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004, pp. 742747.
7. R.E. Fikes, and N.J. Nilsson STRIPS: a new approach to the application of theorem proving to problem solving Artificial Intelligence 2 34 1971 189 208
8. K. Mouro, R. Petrick, M. Steedman, Using kernel perceptrons to learn action effects for planning, in: Proceedings of the International Conference on Cognitive Systems, 2008, pp. 4550.
9. E. Amir, and A. Chang Learning partially observable deterministic action models Journal of Artificial Intelligence Research 33 2008 349 402
10. R. Sutton, Verification, the key to AI, 2001 [Online]. Available from: http://www.cs.ualberta.ca/∼sutton/IncIdeas/KeytoAI.htm.
11. S. Harnad The symbol grounding problem Physica D 42 1990 335 346
12. J. Piaget The Origins of Intelligence in Children 1936 Routledge & Kegan Paul. London French version published in 1936, translation by Margaret Cook published 1952
13. F.J. Corbacho, M.A. Arbib, Schema-based learning: towards a theory of organization for biologically-inspired autonomous agents, in: Proceedings of the First International Conference on Autonomous Agents, 1997, pp. 520521.
14. A. Newell, and H. Simon GPS, a program that simulates human thought E.A. Feigenbaum, J. Feldman, Computers and Thought 1963 McGraw-Hill NY 279 293
15. C. Green Application of theorem proving to problem solving Proceedings of the First International Joint Conference on Artificial Intelligence 1969 Morgan Kaufmann 741 747
16. E.D. Sacerdoti The nonlinear nature of plans Proceedings of the Fourth International Joint Conference on Artificial Intelligence 1975 Morgan Kaufmann 206 214
17. A. Samuel Some studies in machine learning using the game of checkers IBM Journal of Research and Development 3 3 1959 210 229
18. N.J. Nilsson Learning Machines 1965 McGraw-Hill
19. L.P. Kaelbling Learning functions in k-DNF from reinforcement Proceedings of the Seventh International Workshop on Machine Learning 1990 Morgan Kaufmann 162 169
20. T. Mitchel Machine Learning 1997 WCB McGraw Hill
21. H. Pasula, L. Zettlemoyer, and L.P. Kaelbling Learning symbolic models of stochastic domains Journal of Artificial Intelligence 29 2007 309 352 (Pubitemid 47477595)
22. F. Wörgötter, A. Agostini, N. Krger, N. Shyloa, and B. Porr Cognitive agents a procedural perspective relying on the predictability of object-action-complexes (OACs) Robotics and Autonomous Systems 57 4 2009 420 432
23. D. Vernon, G.G. Metta, and G. Sandini A survey of artificial cognitive systems: implications for the autonomous development of mental capabilities in computational agents IEEE Transactions on Evolutionary Computation 11 2007 151 180 (Pubitemid 46547105)
24. L.P. Kaelbling, M.L. Littman, and A.R. Cassandra Planning and acting in partially observable stochastic domains Artificial Intelligence 101 1998 99 134 10.1016/S0004-3702(98)00023-X URL http://portal.acm.org/citation.cfm?id=1643275. 1643301 (Pubitemid 128387390)
25. J. Pearl Probabilistic Reaasoning in Intelligent Systems: Networks of Plausible Inference 1988 Morgan Kaufmann
26. A.P. Dempster, N.M. Laird, and D.B. Rubin Maximum likehood from incomplete data via the EM algorithm Journal of the Royal Statistical Society (Series B) 39 1 1977 1 38
27. D. Spiegelhalter, P. Dawid, S. Lauritzen, and R. Cowell Bayesian analysis in expert systems Statistical Science 8 1993 219 283
28. S. Kok, and P. Domingos Learning the structure of Markov logic networks Proceedings of the Twenty-Second International Conference on Machine Learning 2005 ACM Press 441 448 (Pubitemid 43183364)
29. J.J. Gibson The Perception of the Visual World 1950 Houghton Mifflin Boston
30. E. Sahin, M. akmak, M.R. Doar, E. Uur, and G. coluk To afford or not to afford: a new formalization of affordances toward affordance-based robot control Adaptive Behavior 15 4 2007 447 472 (Pubitemid 350017595)
31. R. Brooks, Intelligence without reason, in: Proceedings of the International Joint Conference on Artificial Intelligence, 1991, pp. 569595.
32. R. Brooks Elephants don't play chess Robotics and Autonomous Systems 6 1&2 1990 3 15 (Pubitemid 20698142)
33. R. Pfeifer, M. Lungarella, and F. Iida Self-organization, embodiment, and biologically inspired robotics Science 318 2007 1088 1093 (Pubitemid 350134861)
34. D. Kraft, N. Pugeault, E. Baeski, M. Popovi, D. Kragic, S. Kalkan, F. Wörgötter, and N. Krger Birth of the object: detection of objectness and extraction of object shape through object action complexes Cognitive Humanoid Robots International Journal of Humanoid Robotics 5 2009 247 265 (special issue)
35. D. Kraft, R. Detry, N. Pugeault, E. Baeski, F. Guerin, J. Piater, and N. Krger Development of object and grasping knowledge by robot exploration IEEE Transactions on Autonomous Mental Development 2 4 2010 368 383
36. A. Stoytchev, Behavior-grounded representation of tool affordances, in: IEEE International Conference on Robotics and Automation, 2005, pp. 30603065.
37. R. Jacobs, M. Jordan, and A. Barto Task decomposition through competition in a modular connectionist architecture: the what and where vision tasks Cognitive Science 15 2 1991 219 250
38. C.M. Vigorito, and A.G. Barto Intrinsically motivated hierarchical skill learning in structured environments IEEE Transactions on Autonomous Mental Development (TAMD) 2 2 2010 132 143
39. K. Narendra, and J. Balakrishnan Adaptive control using multiple models IEEE Transaction on Automatic Control 42 2 1997 171 187 (Pubitemid 127760579)
40. M. Haruno, D. Wolpert, and M. Kawato MOSAIC model for sensorimotor learning and control Neural Computation 13 2001 2201 2220 (Pubitemid 33676881)
41. C. Miall Modular motor learning Trends in Cognitive Sciences 6 1 2002 1 3 (Pubitemid 34081834)
42. J. Gibson The Ecological Approach to Visual Perception 1979 Houghton Mifflin Boston, MA
43. R. Detry, D. Kraft, A.G. Buch, N. Krger, J. Piater, Refining grasp affordance models by experience, in: Proceedings of the IEEE International Conference on Robotics and Automation, 2010, pp. 22872293.
44. T.A. Henzinger The theory of hybrid automata M. Inan, R. Kurshan, Verification of Digital and Hybrid Systems NATO ASI Series F: Computer and Systems Sciences vol. 170 2000 Springer 265 292
45. A.D. Baddeley Essentials of Human Memory 1999 Psychology Press, Taylor and Francis
46. D. Willshaw, P. Buneman, and C. Longuet-Higgins Non-holographic associative memory Nature 222 1969 960 962
47. D. Willshaw Holography, association and induction G. Hinton, J. Anderson, Parallel Models of Associative Memory 1981 Erlbaum Hillsdale, NJ 83 104
48. F.T. Sommer, and G. Palm Bidirectional retrieval from associative memory Advances in Neural Information Processing Systems 10 1998 675 681
49. T. Plate Holographic reduced representations: convolution algebra for compositional distributed representations Proceedings of the Twelfth International Joint Conference on Artificial Intelligence 1991 Morgan Kaufmann San Francisco, CA 30 35
50. D. Omren, A. Ude, A. Kos, Learning primitive actions through object exploration, in: Proceedings of the International Conference on Humanoid Robots, Daejeon, Korea, 2008, pp. 306311.
51. M. Popovi, D. Kraft, L. Bodenhagen, E. Baeski, N. Pugeault, D. Kragic, T. Asfour, and N. Krger A strategy for grasping unknown objects based on co-planarity and colour information Robotics and Autonomous Systems 58 5 2010 551 565
52. L. Bodenhagen, D. Kraft, M. Popovi, E. Baeski, P.E. Hotz, N. Krger, Learning to grasp unknown objects based on 3D edge information, in: Proceedings of the IEEE International Conference on Computational Intelligence in Robotics and Automation, 2009, pp. 421428.
53. N. Pugeault, F. Wörgötter, and N. Krger Visual primitives: local, condensed, semantically rich visual descriptors and their applications in robotics Cognitive Humanoid Vision International Journal of Humanoid Robotics 7 3 2011 379 405 (special issue)
54. R. Detry, N. Pugeault, and J. Piater A probabilistic framework for 3D visual object representation IEEE Transactions on Pattern Analysis and Machine Intelligence 31 10 2009 1790 1803
55. R. Petrick, D. Kraft, K. Mouro, C. Geib, N. Pugeault, N. Krger, M. Steedman, Representation and integration: combining robot control, high-level planning, and action learning, in: Proceedings of the Sixth International Cognitive Robotics Workshop, Patras, Greece, 2008, pp. 3241.
56. R. Petrick, F. Bacchus, A knowledge-based approach to planning with incomplete information and sensing, in: Proceedings of the International Conference on Artificial Intelligence Planning and Scheduling, 2002, pp. 212221.
57. Y. Freund, and R. Schapire Large margin classification using the perceptron algorithm Machine Learning 37 1999 277 296 (Pubitemid 32210619)
58. R. Khardon, and G.M. Wachman Noise tolerant variants of the perceptron algorithm Journal of Machine Learning Research 8 2007 227 248 (Pubitemid 46280179)
59. R. Petrick, D. Kraft, N. Krger, M. Steedman, Combining cognitive vision, knowledge-level planning with sensing, and execution monitoring for effective robot control, in: Proceedings of the Fourth Workshop on Planning and Plan Execution for Real-World Systems, Thessaloniki, Greece, 2009, pp. 5865.