A SOLID Case for Active Bayesian Perception in Robot Touch

  • Nathan F. Lepora
  • Uriel Martinez-Hernandez
  • Tony J. Prescott
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8064)


In a series of papers, we have formalized a Bayesian perception approach for robotics based on recent progress in understanding animal perception. The main principle is to accumulate evidence for multiple perceptual alternatives until reaching a preset belief threshold, formally related to sequential analysis methods for optimal decision making. Here, we extend this approach to active perception, by moving the sensor with a control strategy that depends on the posterior beliefs during decision making. This method can be used to solve problems involving Simultaneous Object Localization and IDentification (SOLID), or ‘where and what’. Considering an example in robot touch, we find that active perception gives an efficient, accurate solution to the SOLID problem for uncertain object locations; in contrast, passive Bayesian perception, which lacked sensorimotor feedback, then performed poorly. Thus, active perception can enable robust sensing in unstructured environments.


Active perception tactile sensing localization robotics 


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  1. 1.
    Bajcsy, R.: Active perception. Proceedings of the IEEE 76, 966–1005 (1988)CrossRefGoogle Scholar
  2. 2.
    Ballard, D.: Animate vision. Artificial Intelligence 48(1), 57–86 (1991)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Metta, G., Sandini, G., Vernon, D., Natale, L., Nori, F.: The icub humanoid robot: an open platform for research in embodied cognition. In: Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems, pp. 50–56 (2008)Google Scholar
  4. 4.
    Kemp, C.C., Edsinger, A., Torres-Jara, E.: Challenges for robot manipulation in human environments (grand challenges of robotics). IEEE Robotics & Automation Magazine 14(1), 20–29 (2007)CrossRefGoogle Scholar
  5. 5.
    Lepora, N.F., Fox, C.W., Evans, M.H., Diamond, M.E., Gurney, K., Prescott, T.J.: Optimal decision-making in mammals: insights from a robot study of rodent texture discrimination. Journal of the Royal Society Interface 9(72), 1517–1528 (2012)CrossRefGoogle Scholar
  6. 6.
    Lepora, N.F., Evans, M., Fox, C.W., Diamond, M.E., Gurney, K., Prescott, T.J.: Naive bayes texture classification applied to whisker data from a moving robot. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2010)Google Scholar
  7. 7.
    Lepora, N.F., Sullivan, J.C., Mitchinson, B., Pearson, M., Gurney, K., Prescott, T.J.: Brain-inspired bayesian perception for biomimetic robot touch. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 5111–5116 (2012)Google Scholar
  8. 8.
    Lepora, N.F., Martinez-Hernandez, U., Barron-Gonzalez, H., Evans, M., Metta, G., Prescott, T.J.: Embodied hyperacuity from bayesian perception: Shape and position discrimination with an icub fingertip sensor. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4638–4643 (2012)Google Scholar
  9. 9.
    Lepora, N.F., Martinez-Hernandez, U., Prescott, T.J.: Active touch for robust perception under position uncertainty. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 3005–3010 (2013)Google Scholar
  10. 10.
    Lepora, N.F., Martinez-Hernandez, U., Prescott, T.J.: Active bayesian perception for simultaneous object localization and identification. In: Robotics: Science and Systems (2013)Google Scholar
  11. 11.
    Gold, J.I., Shadlen, M.N.: The neural basis of decision making. Annual Reviews Neuroscience 30, 535–574 (2007)CrossRefGoogle Scholar
  12. 12.
    Lepora, N.F., Gurney, K.: The basal ganglia optimize decision making over general perceptual hypotheses. Neural Computation 24(11), 2924–2945 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Wald, A.: Sequential analysis. John Wiley and Sons, NY (1947)zbMATHGoogle Scholar
  14. 14.
    Schmitz, A., Maiolino, P., Maggiali, M., Natale, L., Cannata, G., Metta, G.: Methods and technologies for the implementation of large-scale robot tactile sensors. IEEE Transactions on Robotics 27(3), 389–400 (2011)CrossRefGoogle Scholar
  15. 15.
    Evans, M., Fox, C., Lepora, N., Pearson, M., Sullivan, J., Prescott, T.: The effect of whisker movement on radial distance estimation: a case study in comparative robotics. Frontiers in Neurorobotics 6 (2013)Google Scholar
  16. 16.
    Loomis, J.M.: An investigation of tactile hyperacuity. Sensory Processes 3, 289–302 (1979)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nathan F. Lepora
    • 1
  • Uriel Martinez-Hernandez
    • 1
  • Tony J. Prescott
    • 1
  1. 1.Sheffield Center for Robotics (SCentRo)University of SheffieldUK

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