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Robot Trajectory Prediction and Recognition Based on a Computational Mirror Neurons Model

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Artificial Neural Networks and Machine Learning – ICANN 2011 (ICANN 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6792))

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Abstract

Mirror neurons are premotor neurons that are considered to play a role in goal-directed actions, action understanding and even social cognition. As one of the promising research areas in psychology, cognitive neuroscience and cognitive physiology, understanding mirror neurons in a social cognition context, whether with neural or computational models, is still an open issue [5]. In this paper, we mainly focus on the action understanding aspect of mirror neurons, which can be regarded as a fundamental function of social cooperation and social cognition. Our proposed initial architecture is to learn a simulation of the walking pattern of a humanoid robot and to predict where the robot is heading on the basis of its previous walking trajectory.

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References

  1. Cakmak, M., DePalma, N., Arriaga, R., Thomaz, A.L.: Computational benefits of social learning mechanisms: Stimulus enhancement and emulation. In: Proceedings of the 2009 IEEE 8th International Conference on Development and Learning, DEVLRN 2009, pp. 1–7. IEEE Computer Society, Los Alamitos (2009)

    Google Scholar 

  2. Cuijpers, R., Stuijt, F., Sprinkhuizen-Kuyper, I.: Generalisation of action sequences in RNNPB networks with mirror properties. In: Proceedings of the European Symposium on Neural Networks, ESANN (2009)

    Google Scholar 

  3. Demiris, Y., Johnson, M.: Distributed, predictive perception of actions: a biologically inspired robotics architecture for imitation and learning. Connection Science 15(4), 231–243 (2003)

    Article  Google Scholar 

  4. Demiris, Y., Khadhouri, B.: Hierarchical attentive multiple models for execution and recognition of actions. Robotics and Autonomous Systems 54(5), 361–369 (2006)

    Article  Google Scholar 

  5. Dinstein, I., Thomas, C., Behrmann, M., Heeger, D.J.: A mirror up to nature. Current Biology 18(1), R13–R18 (2008)

    Article  Google Scholar 

  6. Elman, J.: Finding structure in time. Cognitive science 14(2), 179–211 (1990)

    Article  Google Scholar 

  7. Elshaw, M., Weber, C., Zochios, A., Wermter, S.: A mirror neuron inspired hierarchical network for action selection. Proc. NeuroBotics, 89–97 (2004)

    Google Scholar 

  8. Fogassi, L., Ferrari, P.F., Gesierich, B., Rozzi, S., Chersi, F., Rizzolatti, G.: Parietal lobe: from action organization to intention understanding. Science 308(5722), 662 (2005)

    Article  Google Scholar 

  9. Haruno, M., Wolpert, D., Kawato, M.: Hierarchical mosaic for movement generation. In: International Congress Series, vol. 1250, pp. 575–590. Elsevier, Amsterdam (2003)

    Google Scholar 

  10. Jellema, T., Baker, C., Oram, M., Perrett, D.: Cell populations in the banks of the superior temporal sulcus of the macaque and imitation. The imitative mind: Evolution, development, and brain bases, 267–290 (2002)

    Google Scholar 

  11. Lange, F.P.d., Spronk, M., Willems, R.M., Toni, I., Bekkering, H.: Complementary systems for understanding action intentions. Current biology 18(6), 454–457 (2008)

    Article  Google Scholar 

  12. Michel, O.: Webots: Professional mobile robot simulation. Journal of Advanced Robotics Systems 1(1), 39–42 (2004)

    MathSciNet  Google Scholar 

  13. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation, pp. 673–695. MIT Press, Cambridge (1988)

    Google Scholar 

  14. Sugita, Y., Tani, J.: Learning semantic combinatoriality from the interaction between linguistic and behavioral processes. Adaptive Behavior 13(1), 33 (2005)

    Article  Google Scholar 

  15. Tani, J., Ito, M., Sugita, Y.: Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB. Neural Networks 17(8-9), 1273–1289 (2004)

    Article  Google Scholar 

  16. Thioux, M., Gazzola, V., Keysers, C.: Action understanding: how, what and why. Current biology 18(10), R431–R434 (2008)

    Article  Google Scholar 

  17. Wermter, S., Weber, C., Elshaw, M., Gallese, V., Pulvermuller, F.: A mirror neuron inspired hierarchical network for action selection. Biomimetic Neural Learning for Intelligent Robots, 162–181 (2005)

    Google Scholar 

  18. Wolpert, D.M., Kawato, M.: Multiple paired forward and inverse models for motor control. Neural Networks 11(7-8), 1317–1329 (1998)

    Article  Google Scholar 

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Zhong, J., Weber, C., Wermter, S. (2011). Robot Trajectory Prediction and Recognition Based on a Computational Mirror Neurons Model. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_43

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  • DOI: https://doi.org/10.1007/978-3-642-21738-8_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21737-1

  • Online ISBN: 978-3-642-21738-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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