Configuration of a Paraconsistent Artificial Neural Network for the Learning from Demonstration Method applied to a Robotic Arm

Authors

DOI:

https://doi.org/10.33448/rsd-v11i7.29720

Keywords:

Paraconsistent Annotated Logic; Learning from demonstration; Artificial Intelligence; Teaching; Paraconsistent artificial neural network.

Abstract

The Annotated Paraconsistent Logic - LPA is a non-classical logic, based on concepts that allow, under certain conditions, to accept the contradiction in its foundations, without invalidating the conclusions. Mathematical interpretations in its associated lattice make it possible to obtain equations and algorithm constructions, which form efficient paraconsistent analysis networks, in treating signals simulating learning. The algorithm used in this research is called Paraconsistent Artificial Neural Cell of Learning (CNAPap), and was created from equations based on LPA. With standardized signals repeatedly applied to its input, CNAPap is capable of gradually storing this information, increasing or decreasing its level of response at the output with asymptotic variation, controlled by a Learning Factor (FA). To run the tests, a set of five CNAPaps forming a learning Paraconsistent Artificial Neural Network (RNAPap), was implemented in an ATMEGA 328p microcontroller and several tests were carried out to validate its operation, acting on learning by demonstration (LfD) in a Robot Manipulator. Considering the fragile mechanical structure of the Robot Manipulator, and the sensor devices adapted to respond to the standards, the laboratory results obtained in the various tests presented were satisfactory, and the microprocessed system built responded efficiently, where the levels of correct answers corresponded to between 75 % to 90%, at all stages of the LfD method. The results of comparative studies showed that RNAPap has dynamic properties capable of acting both in the demonstration learning method and in the imitation method.

Author Biography

Cláudio Luís Magalhães Fernandes, Universidade Santa Cecília

Possui graduação em Engenharia Elétrica Modalidade Eletrônica com Ênfase em Computação pela Universidade Santa Cecilia (2006), Pós Graduação Lato Senso em Automação Industrial pela Faculdade SENAI de Tecnologia Mecatrônica (2010), Pós Graduação Lato Sensu em Docência na Educação Profissional, pelo SENAI CETIQT (2015), Mestrado Profissionalizante em Engenharia Mecânica pela Universidade Santa Cecília (2012) e cursando Doutorado em Engenharia Mecânica na Universidade Santa Cecília. Atualmente é Diretor Acadêmico do Ensino Superior das Faculdades SENAI do estado de São Paulo, Professor da Faculdade de Tecnologia São Vicente dos cursos Tecnólogo em Automação Industrial, Bacharelado em Sistemas de Informação, Engenharia Elétrica. Atua na Universidade Santa Cecília - UNISANTA como pesquisador de técnicas de Inteligência Artificial que fazem uso dos conceitos das Lógicas Não-Clássicas, com ênfase na LPA2V (Lógica Paraconsistente Anotada de dois Valores) e Lógica Fuzzy, aplicadas a sistemas Robóticos e no Controle de Processos Industriais.

References

Abe, J. M., Akama, S., Nakamatsu, K., & Da Silva Filho, J. I. (2018). Some Aspects on Complementarity and Heterodoxy in Non-Classical Logics. Procedia Computer Science, 126, 1253–1260. https://doi.org/10.1016/j.procs.2018.08.068

Mario, M. C., Garcia, D. V., Da Silva Filho, J. I., Silveira Junior, L., & Barbuy, H. S. (2021). Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction. Research, Society and Development, 10(13), e283101320830, https://rsdjournal.org/index.php/rsd/article/view/20830

Andreas, J., Klein, D., & Levine, S. (2017) Modular multitask reinforcement learning with policy sketches. In Proceedings of the 34th International Conference on Machine Learning. 70, 166–175. JMLR. org, 2017. 1, 2

Argall, B. D., Chernova, S., Veloso, M., & Browning, B. (2009). A survey of robot learning from demonstration. Robotics and Autonomous Systems, 57(5), 469-483, https://doi.org/10.1016/j.robot.2008.10.024.

Billard, A., Calinon, S., Dillmann, R., & Schaal, S. (2008). Robot programming by demonstration. In Siciliano, B., and Khatib, O., eds., Springer Handbook of Robotics. Springer Berlin Heidelberg. 2008, 1371–1394.

Chi, M., Yao, Y., Liu, Y., & Zhong, M. Learning, Generalization, and Obstacle Avoidance with Dynamic Movement Primitives and Dynamic Potential Fields. Appl. Sci. 2019, 9, 1535. https://doi.org/10.3390/app9081535

Corrêa, M. P., Machado, A. C., Da Silva Filho, J. I., Garcia, D. V., Mario, M. C., & Sedano, C. T. S. (2022). Paraconsistent annotated logic applied to industry assets condition monitoring and failure prevention based on vibration signatures. Research, Society and Development, [S. l.], 11(1), e14211125104, 2022. 10.33448/rsd-v11i1.25104.

Da Costa N. C. A., & Abe J. M. (2000). Paraconsistência em Informática e Inteligência Artificial, Ciência • Estud. Av. 14 (39) • Https://Doi.Org/10.1590/S0103-40142000000200012

Da Silva Filho, J. I., Abe, J. M., Marreiro, A. D. L., Martinez, A. A. G., Torres, C. R, Rocco, A., Côrtes, H. M., Mario, M. C., Pacheco, M. T. T., Garcia, D. V., & Blos, M. F. (2021) Paraconsistent annotated logic algorithms applied in management and control of communication network routes Sensors, 21(12), 4219 https://doi.org/10.3390/s21124219

Da Silva Filho, J. I., Lambert-Torres, G., & Abe. J. M. (2010). Uncertainty treatment using paraconsistent logic—introducing paraconsistent artificial neural networks. 2010; 320.

Ekvall, S., & Kragic, D. (2008). Robot learning from demonstration: A task-level planning approach. International Journal of Advanced Robotic Systems, 5(3):223234.

Garcia, D. V., Da Silva Filho, J. I., Silveira Jr, L, Pacheco, M. T. T., Abe, J. M., et al. Analysis of Raman spectroscopy data with algorithms based on paraconsistent logic for characterization of skin cancer lesions. Vibrational Spectroscopy 2019;103;102929.

Gienger, M., Mühlig, M., & Steil, J. J. (2010). Imitating object movement skills with robots — A task-level approach exploiting generalization and invariance. 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 1262-1269, 10.1109/IROS.2010.5649990.

Haykin, S. (2001) Redes Neurais: Princípios e Práticas. (2a. ed.): Bookman.

Ijspeert, A. J., Nakanishi, J., & Schaal, S. (2002). Learning rhythmic movements by demonstration using nonlinear oscillators. In Proceedings of the IEEE/RSJ Int. Conference on Intelligent Robots and Systems (IROS2002), 2002, pages 958-963.

Liu, T., & Lemeire, J. (2017) Efficient and Effective Learning of HMMs Based on Identification of Hidden States. Mathematical Problems in Engineering, vol. 2017, Article ID 7318940, 26 pages, 2017. https://doi.org/10.1155/2017/7318940

Mario, M. C., Garcia, D. V., Da Silva Filho, J. I., Silveira Júnior, L., & Barbuy, H. S. (2021). Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction. Research, Society and Development, [S. l.], 10(13), e283101320830, 10.33448/rsd-v10i13.20830.

Mohseni-Kabir, A., Rich, C., Chernova, S., Sidner, C. L., & Miller, D. (2015). Interactive hierarchical task learning from a single demonstration. In Proceedings of the Tenth Annual - ACM/IEEE International Conference on Human-Robot Interaction, HRI ’15, 2015, 205–212. New York, NY, USA: ACM

Nicolescu, M. N., & Mataric, M. J. (2003). Natural methods for robot task learning: Instructive demonstrations, generalization and practice. In Proceedings of the second international joint conference on Autonomous agents and multiagent systems, 2003, 241–248. ACM.

Niekum, S., Osentoski, S., Konidaris, G., Chitta, S., Marthi, B., & Barto, A. G. (2015) Learning grounded finite-state representations from unstructured demonstrations. The International Journal of Robotics Research. 2015; 34(2):131-157. 10.1177/0278364914554471

Pastor, P., Kalakrishnan, M., Meier, F., Stulp, F., Buchli, J., Theodorou, E., & Schaal, S. (2013). From dynamic movement primitives to associative skill memories. Robotics and Autonomous Systems, 2013, 61(4), 351–361.

Rosário, J. M. (2009) Automação Industrial: Editora: Baraúna. 2009. 517 págs. ISBN-13: ‎978-8579230004

Schaal, S. (2006) Dynamic movement primitives-a framework for motor control in humans and humanoid robotics. in Adaptive Motion of Animals and Machines. Springer, 2006, pp. 261–280.

Gebin, L. G. G., Salgado, R. M., & Nogueira, D. A. (2020). Wind Power Forecast: Ensemble Model Based in Statistical and Machine Learning Models. Research, Society and Development, 9(12), e38291211251, 10.33448/rsd-v9i12.11251.

Published

21/05/2022

How to Cite

GOMES, P. M. .; FERNANDES, C. L. M. .; SILVA FILHO, J. I. da .; SILVEIRA, R. S. da .; SANTO, L. do E. .; MARIO, M. C. .; ROSA, V. da S. .; TORRES, G. L. . Configuration of a Paraconsistent Artificial Neural Network for the Learning from Demonstration Method applied to a Robotic Arm. Research, Society and Development, [S. l.], v. 11, n. 7, p. e20911729720, 2022. DOI: 10.33448/rsd-v11i7.29720. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/29720. Acesso em: 20 feb. 2024.

Issue

Section

Engineerings