Bir Robot Sürüsünde Görsel Kopyalama Algoritmasının Analizi

Yıl 2024, , 1789 - 1803, 23.10.2024

Ferhat Demiray , Mehmet Dinçer Erbaş

https://doi.org/10.29130/dubited.1390036

Öz

Bu çalışmada gerçek robotlar üzerinde bir görsel kopyalama algoritması incelenmiş ve bu algoritmayı kullanarak birbirlerinden görsel yolla öğrenen robotların yaptıkları imitasyon hatalarının kökeni araştırılmıştır. İmitasyon hatalarının olası kaynakları olarak, gösterici robotun eyleyicileri ile izleyici robotun algılayıcıları belirlenmiştir. Öncelikle eyleyici kaynaklı hataların miktarı ve sıklığı ölçülmüş ve bu hata türünün minimal ölçüde gözlemlendiği belirtilmiştir. Daha sonra algılayıcı kaynaklı hatalar bir deney senaryosu içerisinde iki farklı güzergâh karşılaştırma metriği kullanılarak incelenmiş ve bu tür hataların kökeni tartışılmıştır. Böylece, gerçek robotlar üzerinde yapılan deneylerde, doğal sistemlerdekine benzer şekilde, imitasyon sırasında ortaya çıkan hatalardan kaynaklı davranış çeşitliliği gözlemlenebildiği belirtilmiştir.

Anahtar Kelimeler

Görsel imitasyon, Çok-robotlu sistemler, Sürü Robotlar

Analysis of a Visual Imitation Algorithm on a Robot Swarm

Öz

In this research, we examined a visual imitation algorithm on a group of real robots and analyzed the source of copying errors that are made by the robots visually learning by using this algorithm. As the two possible sources of the copying errors, the actuators of the demonstrator robot and the sensors of the learner robot were specified. First, it is calculated the amount and frequency of errors due to the actuators and we showed that errors due to actuators of the demonstrator robot were minimal. Second, it is examined the errors due to the sensors by using two different trajectory similarity metric in an experiment scenario and we discussed the origin of this kind of imitation error. In this way, we were able to model a source of behavioral diversity in a robot collective, which is similar to the natural systems, which results from errors that emerge during imitation activity.

Anahtar Kelimeler

Visual imitation, Multi-robot systems, Swarm robotics

Kaynakça

• [1] Zentall, T. R. (1996). ‘’An analysis of imitative learning in animals’’, Social learning in animals: The roots of culture, pp. 221-243.
• [2] Thorndike, E. L. (1898). ''Animal intelligence: an experimental study of the associative processes in animals'', The Psychological Review: Monograph Supplements, vol.2, no.4
• [3] Thorpe, W. H. (1963). Antiphonal singing in birds as evidence for avian auditory reaction time. Nature, vol.197, no.4869, pp. 774-776.
• [4] Mitchell, R. W. (1987). “A comparative-developmental approach to understanding imitation,” Perspectives in Ethology, pp. 183-215, Springer, Boston, MA.
• [5] Zentall, T. R. (2001). “Imitation in animals: evidence, function, and mechanisms,” Cybernetics & Systems, vol. 32, no.1-2, pp. 53-96.
• [6] Zentall, T. R. (2004). “Action imitation in birds,” Animal Learning & Behavior, vol. 32, no. 1, pp. 15-23.
• [7] Myowa‐Yamakoshi, M., Tomonaga, M., Tanaka, M., & Matsuzawa, T. (2004). “Imitation in neonatal chimpanzees (Pan troglodytes),” Developmental Science, vol. 7, no. 4, pp. 437-442.
• [8] Byrne, R. W. (2005). “Social cognition: Imitation, imitation, imitation,” Current Biology, vol. 15, no. 13, pp. R498-R500.
• [9] Heyes, C. M., Dawson, G. R., & Nokes, T. (1992). “Imitation in rats: Initial responding and transfer evidence,” The Quarterly Journal of Experimental Psychology, vol. 45, no. 3, pp. 229-240.
• [10] Tomasello, M., Kruger, A. C., & Ratner, H. H. (1993). “Cultural learning,” Behavioral and Brain Sciences, vol. 16, no. 3, pp. 495-511.
• [11] Meltzoff, A. N. (1988). “The human infant as Homo imitans,” in Social Learning: Psychological and Biological Perspectives, pp. 319-341.
• [12] Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). “Action recognition in the premotor cortex,” Brain, vol. 119, no. 2, pp. 593-609.
• [13] Tomasello, M. (2009). “The usage-based theory of language acquisition,” in The Cambridge Handbook of Child Language, Cambridge Univ. Press, pp. 69-87.
• [14] Kirby, S., Cornish, H., & Smith, K. (2008). “Cumulative cultural evolution in the laboratory: An experimental approach to the origins of structure in human language,” Proceedings of the National Academy of Sciences, vol. 105, no. 31, pp. 10681-10686.
• [15] Cornish, H., Dale, R., Kirby, S., & Christiansen, M. H. (2017). “Sequence memory constraints give rise to language-like structure through iterated learning,” PloS One, vol. 12, no. 1.
• [16] Nehaniv, C. L., & Dautenhahn, K. E. (2007). Imitation and Social Learning in Robots, Humans and Animals: Behavioural, Social and Communicative Dimensions, Cambridge University Press.
• [17] Bakker, P., & Kuniyoshi, Y. (1996, April). “Robot see, robot do: An overview of robot imitation,” in AISB96 Workshop on Learning in Robots and Animals, pp. 3-11.
• [18] Dautenhahn, K., Nehaniv, C. L., & Alissandrakis, A. (2013). “Learning by Experience from Others–Social Learning,” in Adaptivity and Learning: An Interdisciplinary Debate, pp. 217.
• [19] Nehaniv, C. L., & Dautenhahn, K. (2002). “The correspondence problem,” in Imitation in Animals and Artifacts, pp. 41.
• [20] Alissandrakis, A., Nehaniv, C. L., & Dautenhahn, K. (2006, September). “Action, state and effect metrics for robot imitation,” in ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp. 232-237.
• [21] Mochizuki, K., Nishide, S., Okuno, H. G., & Ogata, T. (2013, October). “Developmental human-robot imitation learning of drawing with a neuro dynamical system,” in 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2336-2341.
• [22] Calinon, S., & Billard, A. (2007, March). “Incremental learning of gestures by imitation in a humanoid robot,” in Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 255-262.
• [23] Nicolescu, M., & Mataric, M. J. (2005). “Task learning through imitation and human-robot interaction,” in Models and Mechanisms of Imitation and Social Learning in Robots, Humans and Animals: Behavioural, Social and Communicative Dimensions, pp. 407-424.
• [24] Breazeal, C., Buchsbaum, D., Gray, J., Gatenby, D., & Blumberg, B. (2005). “Learning from and about others: Towards using imitation to bootstrap the social understanding of others by robots,” Artificial Life, vol. 11, no. 1-2, pp. 31-62.
• [25] Billard, A., Calinon, S., Dillmann, R., & Schaal, S. (2008). “Survey: Robot programming by demonstration,” Handbook of Robotics, vol. 59, pp. 1371-1394.
• [26] Mesoudi, A., Whiten, A., & Dunbar, R. (2006). “A bias for social information in human cultural transmission,” British Journal of Psychology, vol. 97, no. 3, pp. 405-423.
• [27] Kalish, M. L., Griffiths, T. L., & Lewandowsky, S. (2007). “Iterated learning: Intergenerational knowledge transmission reveals inductive biases,” Psychonomic Bulletin & Review, vol. 14, no. 2, pp. 288-294.
• [28] Xu, J., Dowman, M., & Griffiths, T. L. (2013). “Cultural transmission results in convergence towards colour term universals,” Proceedings of the Royal Society B: Biological Sciences, vol. 280, no. 1758, 20123073.
• [29] Kirby, S. (2001). “Spontaneous evolution of linguistic structure-an iterated learning model of the emergence of regularity and irregularity,” IEEE Transactions on Evolutionary Computation, vol. 5, no. 2, pp. 102-110.
• [30] Steels, L. (2003). “Evolving grounded communication for robots,” Trends in Cognitive Sciences, vol. 7, no. 7, pp. 308-312.
• [31] Steels, L., & Spranger, M. (2012). “Emergent mirror systems for body language,” Experiments in Cultural Language Evolution, pp. 87-109.
• [32] Erbas, M. D. (2019). “The development of a robust symbolic communication system for robots via embodied iterated imitation,” Adaptive Behavior, vol. 27, no. 2, pp. 137-156.
• [33] Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., ... & Martinoli, A. (2009). “The e-puck, a robot designed for education in engineering,” in Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, vol. 1, no. CONF, pp. 59-65.
• [34] Liu, W., & Winfield, A. F. (2011). “Open-hardware e-puck Linux extension board for experimental swarm robotics research,” Microprocessors and Microsystems, vol. 35, no. 1, pp. 60-67.
• [35] Stanford Artificial Intelligence Laboratory et al. (2018). “Robotic Operating System,” retrieved from [https://www.ros.org].
• [36] Seber, G. A., & Lee, A. J. (2012). Linear Regression Analysis, vol. 329, John Wiley & Sons.
• [37] Toohey, K., & Duckham, M. (2015). “Trajectory similarity measures,” Sigspatial Special, vol. 7, no.1, pp. 43-50.
• [38] Chen, L., & Ng, R. (2004). “On the marriage of lp-norms and edit distance,” in Proceedings of the Thirtieth International Conference on Very Large Data Bases, pp. 792-803.