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Nesne-Yönelimli eylem tahmini gelişimi için hesaplamalı model

Year 2020, Volume: 26 Issue: 5, 974 - 982, 23.10.2020

Abstract

Deneysel psikoloji alanındaki son çalışmalar, başkalarının eylem sonuçlarını tahmin edebilme yeteneğinin gelişimi ile aynı eylemi gerçekleştirebilecek motor becerilerin gelişimi arasında doğrudan bir bağlantı olduğunu göstermiştir. Bebekler doğum sonrası ilk yıl içerisinde çevreleriyle etkileşime geçerler ve bu etkileşimleri sonucu bazı beceriler ve yetenekler kazanırlar. Aynı zamanda bu süreç içerisinde çevrelerindeki başka insanlarla da etkileşime geçip başkalarının eylem amaçları hakkında fikir sahibi olurlar. Bu çalışma, masa üstü nesnelere yönelik kavrama ve itme eylemlerinin sonuç tahminlerinin gelişimsel sürecini inceleyebilmek adına hesaplamalı bir model önermektedir. Bu bağlamda belirli nesnelere doğru yörüngeler oluşturulmuş ve bir veri kümesinden alınan el resimlerinin de yardımıyla kavrama ve itme eylemlerinin hedefindeki nesneler yörünge tahminiyle saptanmaya çalışılmıştır. Kavrama ve itme eylemlerine yönelik gelişimsel süreç dört evrede analiz edilmiştir. Sonuçlar, ortaya koyulan hesaplamalı modelin eylem tanınırlığı ile eylem sonuç tahmin yeteneği arasında doğrudan bir ilişki kurabildiğini ve kavrama eylemine ait tahmin yeteneğinin daha erken geliştiğini göstermektedir.

References

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  • [2] Robson SJ, Kuhlmeier VA. “Infants’ understanding of object-directed action: An interdisciplinary synthesis”. Frontiers in Psychology, 7, 1-14, 2016
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  • [5] Kraft D, Detry R. “Development of Object and Grasping Knowledge by Robot Exploration”. IEEE Transactions on Autonomous Mental Development, 2(4), 368-383, 2011.
  • [6] Oztop E, Bradley NS, Arbib MA. “Infant grasp learning: a computational model”. Experimental Brain Research, 158(4), 480-503, 2004.
  • [7] Kawato M. “Internal models for motor control and trajectory planning”. Current Opinion in Neurobiology, 9(6), 718-727, 1999.
  • [8] Schmidhuber J. “Learning factorial codes by predictability minimization”. Neural Computation, 4(6), 863-879, 1992.
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  • [19] Hamlin J, Hallinan EV, Woodward AL. “Do as i do: 7-month-old infants selectively reproduce others’ goals”. Developmental Science, 11(4), 487-94, 2008.
  • [20] Dogar MR, Srinivasa SS. “Push-grasping with dexterous hands: Mechanics and a method”. IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, 18-22 October 2010.
  • [21] Natale L, Metta G, Sandini G. “A developmental approach to grasping”. Developmental robotics AAAI spring symposium, California, USA, 21-23 March 2005.
  • [22] Juett J, Kuipers Benjamin. “Learning and Acting in Peripersonal Space: Moving, Reaching, and Grasping”. Frontiers in Neurorobotics, 13, 4, 2019.
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  • [27] Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. Editors: McClelland JL, Rumelhart DE. Parallel Distributed Processing Explorations in the Microstructure of Cognition Foundations, 318-362, California, USA, MIT Press, 1985.
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  • [29] Finn C, Tan XY, Duan Y, Darrell T, Levine S, Abbeel P. “Deep spatial autoencoders for visuomotor learning”. IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 16-20 May 2016.
  • [30] Watter M, Springenberg J, Boedecker J, Riedmiller M. “Embed to control: A locally linear latent dynamics model for control from raw images”. In Advances in neural information processing systems, Montreal, Canada, 07-12 December 2015.
  • [31] Hubel DH, Wiesel TN. “Receptive fields and functional architecture of monkey striate cortex”. The Journal of physiology, 195(1), 215-243, 1968.
  • [32] Fukushima K. “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position”. Biological Cybernetics, 36(4), 193-202, 1980.
  • [33] Yamashita R, Nishio M, Do RKG, Togashi K. “Convolutional neural networks: an overview and application in radiology”. Insights into imaging, 9(4), 611-629, 2018.
  • [34] Maaten LVD, Hinton G. “Visualizing data using t-SNE”. Journal of machine learning research, 9, 2579-2605, 2008.
  • [35] Hochreiter S, Schmidhuber J. “Long short-term memory”. Neural computation, 9(8), 1735-1780, 1997.
  • [36] Seker MY, Tekden AE, Ugur E. “Deep effect trajectory prediction in robot manipulation”. Robotics and Autonomous Systems, 119, 173-184, 2019.
  • [37] Zhang P, Ouyang W, Zhang P, Xue J, Zheng N. “SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, California, USA, 16-20 June, 2019.
  • [38] Oudeyer P, Kaplan F, Hafner VF. “Intrinsic Motivation Systems for Autonomous Mental Development”. IEEE Transactions on Evolutionary Computation, 11(2), 265-286, 2007.
Year 2020, Volume: 26 Issue: 5, 974 - 982, 23.10.2020

Abstract

References

  • [1] Kanakogi Y, Itakura S. “Developmental correspondence between action prediction and motor ability in early infancy”. Nature Communications, 2(1), 1-6, 2011.
  • [2] Robson SJ, Kuhlmeier VA. “Infants’ understanding of object-directed action: An interdisciplinary synthesis”. Frontiers in Psychology, 7, 1-14, 2016
  • [3] Caligiore D, Baldassarre G. “The development of reaching and grasping: towards an integrated framework based on a critical review of computational and robotic models”. Reach-to-Grasp Behavior, 319-348, Routledge, 2018.
  • [4] Ryan RM, Deci EL. “Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being”. American psychologist, 55(1), 68, 2000. ”
  • [5] Kraft D, Detry R. “Development of Object and Grasping Knowledge by Robot Exploration”. IEEE Transactions on Autonomous Mental Development, 2(4), 368-383, 2011.
  • [6] Oztop E, Bradley NS, Arbib MA. “Infant grasp learning: a computational model”. Experimental Brain Research, 158(4), 480-503, 2004.
  • [7] Kawato M. “Internal models for motor control and trajectory planning”. Current Opinion in Neurobiology, 9(6), 718-727, 1999.
  • [8] Schmidhuber J. “Learning factorial codes by predictability minimization”. Neural Computation, 4(6), 863-879, 1992.
  • [9] de Bruin L, Michael J. “Prediction error minimization as a framework for social cognition research”. Erkenntnis, https://doi.org/10.1007/s10670-018-0090-9, 2018.
  • [10] Sommerville JA, Woodward AL, Needham A. “Action experience alters 3-month-old infants’ perception of others’ actions”. Cognition, 96(1), 1-11, 2005.
  • [11] Daum MM, Prinz W, Aschersleben G. “Encoding the goal of an object-directed but uncompleted reaching action in 6-and 9-month-old infants”. Developmental Science, 11(4), 607-619, 2008.
  • [12] Rajmohan V, Mohandas E. “Mirror neuron system”. Indian journal of psychiatry, 49(1), 66, 2007.
  • [13] Gallese V, Fadiga L, Fogassi L, Rizzolatti G. “Action recognition in the premotor cortex”. Brain, 119(2), 593-609, 1996.
  • [14] Rizzolatti G, Fogas si L, Gallese V. “Neurophysiological mechanisms underlying the understanding and imitation of action”. Nature reviews neuroscience, 2(9), 661-670, 2001.
  • [15] Copete JL, Nagai Y, Asada M. “Motor development facilitates the prediction of others’ actions through sensorimotor predictive learning”. Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE, Cergy-Pontoise, France, 19-22 September 2016.
  • [16] Flash T, Hogan N. “The coordination of arm movements: an experimentally confirmed mathematical model”. Journal of neuroscience, 5(7), 1688-1703, 1985.
  • [17] Woodward AL. “Infants’ ability to distinguish between purposefuland non-purposeful behaviors”. Infant Behavior and Development, 22(2), 145-160, 1999.
  • [18] Gredebäck G, Stasiewicz D, Falck-Ytter T, Hofsten CV, Rosander K. “Action type and goal type modulate goal-directed gaze shifts in 14-month-old infants”. Developmental psychology, 45(4), 1190, 2009.
  • [19] Hamlin J, Hallinan EV, Woodward AL. “Do as i do: 7-month-old infants selectively reproduce others’ goals”. Developmental Science, 11(4), 487-94, 2008.
  • [20] Dogar MR, Srinivasa SS. “Push-grasping with dexterous hands: Mechanics and a method”. IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, 18-22 October 2010.
  • [21] Natale L, Metta G, Sandini G. “A developmental approach to grasping”. Developmental robotics AAAI spring symposium, California, USA, 21-23 March 2005.
  • [22] Juett J, Kuipers Benjamin. “Learning and Acting in Peripersonal Space: Moving, Reaching, and Grasping”. Frontiers in Neurorobotics, 13, 4, 2019.
  • [23] Coumans E. “Bullet physics simulation”. In ACM SIGGRAPH Courses, 2015.
  • [24] Rohmer E, Singh SP, Freese M. “V-REP: A versatile and scalable robot simulation framework”. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, 03-07 November 2013.
  • [25] Marcel S, Bernier O. “Hand Posture Recognition in a Body-Face Centered Space”. Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction, London, UK, 17-19 March 1999.
  • [26] Baldi P. “Autoencoders, unsupervised learning, and deep architectures”. In Proceedings of ICML workshop on unsupervised and transfer learning, Washington, USA, 02 July 2012.
  • [27] Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. Editors: McClelland JL, Rumelhart DE. Parallel Distributed Processing Explorations in the Microstructure of Cognition Foundations, 318-362, California, USA, MIT Press, 1985.
  • [28] Ha D, Schmidhuber J. “Recurrent world models facilitate policy evolution”. In Advances in Neural Information Processing Systems, Montreal, Canada 02-08 December 2018.
  • [29] Finn C, Tan XY, Duan Y, Darrell T, Levine S, Abbeel P. “Deep spatial autoencoders for visuomotor learning”. IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 16-20 May 2016.
  • [30] Watter M, Springenberg J, Boedecker J, Riedmiller M. “Embed to control: A locally linear latent dynamics model for control from raw images”. In Advances in neural information processing systems, Montreal, Canada, 07-12 December 2015.
  • [31] Hubel DH, Wiesel TN. “Receptive fields and functional architecture of monkey striate cortex”. The Journal of physiology, 195(1), 215-243, 1968.
  • [32] Fukushima K. “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position”. Biological Cybernetics, 36(4), 193-202, 1980.
  • [33] Yamashita R, Nishio M, Do RKG, Togashi K. “Convolutional neural networks: an overview and application in radiology”. Insights into imaging, 9(4), 611-629, 2018.
  • [34] Maaten LVD, Hinton G. “Visualizing data using t-SNE”. Journal of machine learning research, 9, 2579-2605, 2008.
  • [35] Hochreiter S, Schmidhuber J. “Long short-term memory”. Neural computation, 9(8), 1735-1780, 1997.
  • [36] Seker MY, Tekden AE, Ugur E. “Deep effect trajectory prediction in robot manipulation”. Robotics and Autonomous Systems, 119, 173-184, 2019.
  • [37] Zhang P, Ouyang W, Zhang P, Xue J, Zheng N. “SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, California, USA, 16-20 June, 2019.
  • [38] Oudeyer P, Kaplan F, Hafner VF. “Intrinsic Motivation Systems for Autonomous Mental Development”. IEEE Transactions on Evolutionary Computation, 11(2), 265-286, 2007.
There are 38 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Article
Authors

Serkan Buğur This is me

Emre Uğur This is me

Publication Date October 23, 2020
Published in Issue Year 2020 Volume: 26 Issue: 5

Cite

APA Buğur, S., & Uğur, E. (2020). Nesne-Yönelimli eylem tahmini gelişimi için hesaplamalı model. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 26(5), 974-982.
AMA Buğur S, Uğur E. Nesne-Yönelimli eylem tahmini gelişimi için hesaplamalı model. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. October 2020;26(5):974-982.
Chicago Buğur, Serkan, and Emre Uğur. “Nesne-Yönelimli Eylem Tahmini gelişimi için Hesaplamalı Model”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 26, no. 5 (October 2020): 974-82.
EndNote Buğur S, Uğur E (October 1, 2020) Nesne-Yönelimli eylem tahmini gelişimi için hesaplamalı model. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 26 5 974–982.
IEEE S. Buğur and E. Uğur, “Nesne-Yönelimli eylem tahmini gelişimi için hesaplamalı model”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 26, no. 5, pp. 974–982, 2020.
ISNAD Buğur, Serkan - Uğur, Emre. “Nesne-Yönelimli Eylem Tahmini gelişimi için Hesaplamalı Model”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 26/5 (October 2020), 974-982.
JAMA Buğur S, Uğur E. Nesne-Yönelimli eylem tahmini gelişimi için hesaplamalı model. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2020;26:974–982.
MLA Buğur, Serkan and Emre Uğur. “Nesne-Yönelimli Eylem Tahmini gelişimi için Hesaplamalı Model”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 26, no. 5, 2020, pp. 974-82.
Vancouver Buğur S, Uğur E. Nesne-Yönelimli eylem tahmini gelişimi için hesaplamalı model. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2020;26(5):974-82.

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