Perception and Cognition: Two Foremost Ingredients toward Autonomous Intelligent Robots

Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 174)

Abstract

Inspired by early-ages human’s skills developments, the present paper accosts the robots’ intelligence from a different slant directing the attention to both “cognitive” and “perceptual” abilities. the machine’s (robot’s) shrewdness is constructed on the basis of a Multi-level cognitive concept attempting to handle complex artificial behaviors. The intended complex behavior is the autonomous discovering of objects by robot exploring an unknown environment.

Keywords

Humanoid Robot Elementary Function Salient Object Salient Region Biped Robot 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Westervelt, E.R., Buche, G., Grizzle, J.W.: Experimental validation of a framework for the design of controllers that induce stable walking in planar bipeds. International J. of Robotics Research 23(6), 559–582 (2004)CrossRefGoogle Scholar
  2. 2.
    Park, J.H., Kwon, O.: Reflex Control of Biped Robot Locomotion on a Slippery Surface. In: Proc. IEEE Conf. on Robotics and Automation, pp. 4134–4139 (2001)Google Scholar
  3. 3.
    Kuffner, K., Nishiwaki, S., Kagami, M., Inaba, H.: Inoue. Footstep Planning Among Obstacles for Biped Robots. In: Proceedings of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 500–505 (2001)Google Scholar
  4. 4.
    Chestnutt, J., Kuffner, J.J.: A Tiered Planning Strategy for Biped Navigation. In: Int. Conf. on Humanoid Robots (Humanoids 2004), Proceedings, vol. 1, pp. 422–436 (2004)Google Scholar
  5. 5.
    Huang, Q., Yokoi, K., Kajita, S., Kaneko, K., Arai, H., Koyachi, N., Tanie, K.: Planning walking patterns for a biped robot. IEEE Transac. on Robotics and Automation 17(3), 280–289 (2001)CrossRefGoogle Scholar
  6. 6.
    Sabe, K., Fukuchi, M., Gutmann, J., Ohashi, T., Kawamoto, K., Yoshigahara, T.: Obstacle Avoidance and Path Planning for Humanoid Robots using Stereo Vision. In: Int. Conf. on Robotics Automation (ICRA), pp. 592–597 (2004)Google Scholar
  7. 7.
    Holmes, R.: Acts of War: The Behavior of Men in Battle (First American Edition). The Free Press, New York (1985)Google Scholar
  8. 8.
    Tambe, M., Johnson, W., Jones, R., Koss, F., Laird, J., Rosenbloom, P., Schwamb, K.: Intelligent Agents for Interactive Simulation Environments. AI Magazine 16(1), 15–40 (1995)Google Scholar
  9. 9.
    Langley, P.: An abstract computational model of learning selective sensing skills. In: Proceedings of the 18th Conference of the Cognitive Science Society, pp. 385–390 (1996)Google Scholar
  10. 10.
    Bauckhage, C., Thurau, C., Sagerer, G.: Learning Human-Like Opponent Behavior for Interactive Computer Games. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 148–155. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Potkonjak, V., Kostic, D., Tzafestas, S., Popovic, M., Lazarevic, M., Djordjevic, G.: Human-like behavior of robot arms: general considerations and the handwriting task. Robotics and Computer-Integrated Manufacturing 17(4), 317–327 (2001)CrossRefGoogle Scholar
  12. 12.
    Edlund, J., Gustafson, J., Heldner, M., Hjalmarsson, A.: Towards human-like spoken dialogue systems. J. Speech Communication 50(8-9), 630–645 (2008)CrossRefGoogle Scholar
  13. 13.
    Lubin, A., Poirel, N., Rossi, S., Pineau, A., Houdé, O.: Math in actions: Actor mode reveals the true arithmetic abilities of French-speaking two-year-olds in a magic task. J. of Experimental Child Psychology (103), 376–385 (2009)CrossRefGoogle Scholar
  14. 14.
    Campbell, F.A., Pungello, E.P., Miller-Johnson, S., Burchinal, M., Ramey, C.T.: The development of cognitive and academic abilities: growth curves from an early childhood educational experiment. Dev. Psychol. 37(2), 231–242 (2001)CrossRefGoogle Scholar
  15. 15.
    Leroux, G., Joliot, M., Dubal, S., Mazoyer, B., Tzourio-Mazoyer, N., Houdé, O.: Cognitive inhibition of number/length interference in a Piaget-like task: Evidence from ERP and fMRI. Human Brain Mapping (27), 498–509 (2006)CrossRefGoogle Scholar
  16. 16.
    Lubin, A., Poirel, N., Rossi, S., Lanoé, C., Pineau, A., Houdé, O.: Pedagogical effect of action on arithmetic performances in Wynn-like tasks solved by 2-year-olds. Experimental Psychology (2010)Google Scholar
  17. 17.
    Cassell, O.C., Hubble, M., Milling, M.A., Dickson, W.A.: Baby walkers; still a major cause of infant burns. Burns 23, 451–453 (1997)CrossRefGoogle Scholar
  18. 18.
    Crouchman, M.: The effects of babywalkers on early locomotor development. Developmental Medicine and Child Neurology (8), 757–761 (1986)Google Scholar
  19. 19.
    Siegel, A., Burton, R.: Effects of babywalkers on early locomotor development in human infants. Dev. Behav. Pediatr. (20), 355–361 (1999)CrossRefGoogle Scholar
  20. 20.
    Kauffmann, I., Ridenour, M.: Influence of an infant walker on onset and quality of walking pattern of locomotion: an electromyographic investigation. Percept Motor Skills (45), 1323–1329 (1987)CrossRefGoogle Scholar
  21. 21.
    Madani, K., Sabourin, C.: Multi-level cognitive machine-learning based concept for human-like “artificial” walking: application to autonomous stroll of humanoid robots. Neurocomuting 74, 1213–1228 (2011)CrossRefGoogle Scholar
  22. 22.
    Bülthoff, H., Wallraven, C., Giese, M.: Perceptual Robotic. In: Siciliano, B., Khatib, O. (eds.) Handbook of Robotics. Springer (2007)Google Scholar
  23. 23.
  24. 24.
    Zukow-Goldring, P., Arbib, M.A.: Affordances, effectivities, and assisted imitation: Caregivers and the directing of attention. Neurocomputing 70, 2181–2193 (2007)CrossRefGoogle Scholar
  25. 25.
    Brand, R.J., Baldwin, D.A., Ashburn, L.A.: Evidence for ‘motionese’: modifications in mothers infant-directed action. Developmental Science 5, 72–83 (2002)CrossRefGoogle Scholar
  26. 26.
    Achanta, R., Hemami, S., Estrada, E., Susstrunk, S.: Frequency-tuned Salient Region Detection. In: IEEE Inrernat. Conf. on Compurer Vision & Pattern Recognition, CVPR (2009)Google Scholar
  27. 27.
    Wolfe, J.M., Horowitz, T.S.: What attributes guide the deployment of visual attention and how do they do it. Nature Reviews Neuroscience 5, 495–501 (2004)CrossRefGoogle Scholar
  28. 28.
    Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. IEEE Conference on Computer Vision and Pattern Recognition 2(800), 1–8 (2007)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Moreno, R., Graña, M., Ramik, D.M., Madani, K.: Image segmentation by spherical coordinates. In: Proc. of 11th Internat. Conf. on Pattern Recognition and Information Processing (PRIP 2011), pp. 112–115 (2011)Google Scholar
  30. 30.
    Holland, J.H.: Adaptation in Natural anti Artificial Systems: An introductory Analysis with Applications to Biology. In: Control and Artificial Intelligence. MIT Press (1992)Google Scholar
  31. 31.
    Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.-Y.: Learning to detect a salient object. JEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)CrossRefGoogle Scholar
  32. 32.
    Ramík, D.M., Sabourin, C., Madani, K.: Hybrid Salient Object Extraction Approach with Automatic Estimation of Visual Attention Scale. In: Proc. 7th Internat. Conf. on Signal Image Technology & Internet-Based Systems (IEEE – SITIS 2011), pp. 438–445 (2011)Google Scholar
  33. 33.
    Ramík, D.M., Sabourin, C., Madani, K.: A Cognitive Approach for Robots’ Vision Using Unsupervised Learning and Visual Saliency. In: Cabestany, J., Rojas, I., Joya, G. (eds.) IWANN 2011, Part I. LNCS, vol. 6691, pp. 81–88. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Images, Signals and Intelligence Systems Laboratory (LISSI / EA 3956)University PARIS-EST Creteil (UPEC), Senart-FB Institute of TechnologyLieusaintFrance

Personalised recommendations