What You Choose to See Is What You Get: An Experiment with Learnt Sensory Modulation in a Robotic Foraging Task

  • Tiago Rodrigues
  • Miguel Duarte
  • Sancho Oliveira
  • Anders Lyhne Christensen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8602)

Abstract

In evolutionary robotics, the mapping from raw sensory input to neural network input is typically decided by the experimenter or encoded in the genome. Either way, the mapping remains fixed throughout a robot’s lifetime. Inspired by biological sensory organs and the mammalian brain’s capacity for selective attention, we evaluate an alternative approach in which a robot has active, real-time control over the mapping from sensory input to neural network input. We augment the neural controllers with additional output neurons that control key sensory parameters and evolve solutions for a single-robot foraging task. The results show that the capacity to control the mapping from raw input to neural network input is exploited by evolution and leads to novel solutions with higher fitness compared to traditional approaches.

Keywords

Evolutionary robotics dynamic sensors sensor evolution genome-encoding 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ampatzis, C., Tuci, E., Trianni, V., Christensen, A.L., Dorigo, M.: Evolving self-assembly in autonomous homogeneous robots: experiments with two physical robots. Artificial Life 15(4), 465–484 (2009)CrossRefGoogle Scholar
  2. 2.
    Auerbach, J.E., Bongard, J.C.: On the relationship between environmental and mechanical complexity in evolved robots. In: International Conference on Artificial Life (ALIFE), pp. 309–316. MIT Press, Cambridge (2012)Google Scholar
  3. 3.
    Balakrishnan, K., Honavar, V.: On sensor evolution in robotics. In: Annual Conference on Genetic Programming, pp. 455–460. MIT Press, Cambridge (1996)Google Scholar
  4. 4.
    Beer, R.D., Gallagher, J.C.: Evolving dynamical neural networks for adaptive behavior. Adaptive Behavior 1, 91–122 (1992)CrossRefGoogle Scholar
  5. 5.
    Bellman, R.: Dynamic Programming, 1st edn. Princeton University Press, Princeton (1957)MATHGoogle Scholar
  6. 6.
    Dorigo, M., Floreano, D., Gambardella, L.M., Mondada, F., Nolfi, S., Baaboura, T., Birattari, M., Bonani, M., Brambilla, M., Brutschy, A., et al.: Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robotics & Automation Magazine 20(4), 60–71 (2013)CrossRefGoogle Scholar
  7. 7.
    Floreano, D., Dürr, P., Mattiussi, C.: Neuroevolution: from architectures to learning. Evolutionary Intelligence 1(1), 47–62 (2008)CrossRefGoogle Scholar
  8. 8.
    Floreano, D., Mondada, F.: Evolutionary neurocontrollers for autonomous mobile robots. Neural Networks 11(7–8), 1461–1478 (1998)CrossRefGoogle Scholar
  9. 9.
    Fries, P., Reynolds, J.H., Rorie, A.E., Desimone, R.: Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291(5508), 1560–1563 (2001)CrossRefGoogle Scholar
  10. 10.
    Groot, S.G.D., Gebhard, J.W.: Pupil size as determined by adapting luminance. Journal of the Optical Society of America 42(7), 492–495 (1952)CrossRefGoogle Scholar
  11. 11.
    Hess, E.H., Polt, J.M.: Pupil size as related to interest value of visual stimuli. Science 132(3423), 349–350 (1960)CrossRefGoogle Scholar
  12. 12.
    Kam-Chuen, J., Giles, C., Horne, B.: An analysis of noise in recurrent neural networks: convergence and generalization. IEEE Transactions on Neural Networks 7(6), 1424–1438 (1996)CrossRefGoogle Scholar
  13. 13.
    Lipson, H., Pollack, J.B.: Automatic design and manufacture of robotic lifeforms. Nature 406(6799), 974–978 (2000)CrossRefGoogle Scholar
  14. 14.
    Lund, H., Hallam, J., Lee, W.-P.: Evolving robot morphology. In: IEEE International Conference on Evolutionary Computation, pp. 197–202. IEEE Press, Piscataway (1997)Google Scholar
  15. 15.
    Mark, A., Mark, R., Polani, D., Uthmann, T.: A framework for sensor evolution in a population of braitenberg vehicle-like agents. In: International Conference on Artificial Life (ALIFE), pp. 428–432. MIT Press, Cambridge (1998)Google Scholar
  16. 16.
    Mautner, C., Belew, R.K.: Evolving robot morphology and control. Artificial Life and Robotics 4(3), 130–136 (2000)CrossRefGoogle Scholar
  17. 17.
    Meyer, J.-A., Husbands, P., Harvey, I.: Evolutionary robotics: A survey of applications and problems. In: 1st European Workshop on Evolutionary Robotics (EvoRobot), pp. 1–21. Springer, Berlin (1998)Google Scholar
  18. 18.
    Mondada, F., Guignard, A., Bonani, M., Bär, D., Lauria, M., Floreano, D.: Swarm-bot: From concept to implementation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1626–1631. IEEE Press, Piscataway (2003)Google Scholar
  19. 19.
    Nolfi, S., Floreano, D.: Learning and evolution. Autonomous Robots 7(1), 89–113 (1999)CrossRefGoogle Scholar
  20. 20.
    Nolfi, S., Floreano, D.: Evolutionary robotics: The biology, intelligence, and technology of self-organizing machines. MIT Press, Cambridge (2000)Google Scholar
  21. 21.
    Parker, G., Nathan, P.: Concurrently evolving sensor morphology and control for a hexapod robot. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–6. IEEE Press, Piscataway (2010)Google Scholar
  22. 22.
    Silva, F., Urbano, P., Oliveira, S., Christensen, A.L.: odNEAT: An algorithm for distributed online, onboard evolution of robot behaviours. In: International Conference on Simulation and Synthesis of Living Systems (ALIFE), pp. 251–258. MIT Press, Cambridge (2012)Google Scholar
  23. 23.
    Soltoggio, A., Bullinaria, J.A., Mattiussi, C., Dürr, P., Floreano, D.: Evolutionary advantages of neuromodulated plasticity in dynamic, reward-based scenarios. In: International Conference on the Simulation and Synthesis of Living Systems (ALIFE), pp. 569–576. MIT Press, Cambridge (2008)Google Scholar
  24. 24.
    Watson, R., Ficici, S., Pollack, J.: Embodied evolution: Embodying an evolutionary algorithm in a population of robots. In: IEEE Congress on Evolutionary Computation (CEC), pp. 335–342. IEEE Press, Piscataway (1999)Google Scholar
  25. 25.
    Young, E.D., Rice, J.J., Tong, S.C.: Effects of pinna position on head-related transfer functions in the cat. Journal of the Acoustical Society of America 99(5), 3064–3076 (1996)CrossRefGoogle Scholar
  26. 26.
    Duarte, M., Sliva, F., Rodrigues, T., Oliveria, S.M., Christensen, A.L.: JBotEvolver: A Versatile Simulation Platform for Evolutionary Robotics. Proceedings of the International Conference on the Synthesis and Simulation of Living System (ALIFE), pp. 210–211. MIT Press, Cambridge, MA (2014)Google Scholar
  27. 27.
    Zhang, Y., Martinoli, A., Antonsson, E.K.: Evolutionary design of a collective sensory system. In: AAAI Spring Symposium on Computational Synthesis, pp. 283–290. MIT Press, Cambridge (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Tiago Rodrigues
    • 1
  • Miguel Duarte
    • 1
  • Sancho Oliveira
    • 1
  • Anders Lyhne Christensen
    • 1
  1. 1.Instituto de Telecomunicações & Instituto Universitário de Lisboa (ISCTE-IUL)LisbonPortugal

Personalised recommendations