Abstract
In conventional “sense-think-act” control architectures, perception is reduced to a passive collection of sensory information, followed by a mapping onto a prestructured internal world model. For biological agents, Sensorimotor Contingency Theory (SMCT) posits that perception is not an isolated processing step, but is constituted by knowing and exercising the law-like relations between actions and resulting changes in sensory stimulation. We present a computational model of SMCT for controlling the behavior of a quadruped robot running on different terrains. Our experimental study demonstrates that: (i) Sensory-Motor Contingencies (SMC) provide better discrimination capabilities of environmental properties than conventional recognition from the sensory signals alone; (ii) discrimination is further improved by considering the action context on a longer time scale; (iii) the robot can utilize this knowledge to adapt its behavior for maximizing its stability.
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References
Bagnell, J.A., Bradley, D., Silver, D., Sofman, B., Stentz, A.: Learning for autonomous navigation. IEEE Robotics & Automation Magazine 17(2), 74–84 (2010)
Beer, R.D.: The dynamics of active categorical perception in an evolved model agent. Adaptive Behavior 11, 209–243 (2003)
Dewey, J.: The reflex arc concept in psychology. Psychological Review 3, 357–370 (1896)
Engel, A.K.: Directive minds: how dynamics shapes cognition. In: Stewart, J., Gapenne, O., Di Paolo, E.A. (eds.) Enaction: Towards a New Paradigm for Cognitive Science, pp. 219–243. MIT Press, Cambridge (2011)
Gibson, E.J., Riccio, G., Schmuckler, M.A., Stoffregen, T.A., Rosenberg, D., Taromina, J.: Detection of traversability of surfaces by crawling and walking infants. Journal of Experimental Psychology 13(4), 533–544 (1987)
Giguere, P., Dudek, G.: Clustering sensor data for autonomous terrain identification using time-dependency. Autonomous Robots 26, 171–186 (2009)
Harnad, S.: Cognition is categorization. In: Handbook of Categorization in Cognitive Science. Elsevier (2005)
Hoffmann, M., Pfeifer, R.: The implications of embodiment for behavior and cognition: animal and robotic case studies. In: The Implications of Embodiment: Cognition and Communication, pp. 31–58. Imprint Academic (2011)
Jackel, L.D., Krotkov, E., Perschbacher, M., Pippine, J., Sullivan, C.: The DARPA LAGR program: goals, challenges, methodology, and phase I results. Journal of Field Robotics 23(11/12), 945–973 (2006)
Lungarella, M., Sporns, O.: Mapping information flow in sensorimotor networks. PLoS Computational Biology 2 e144(10), 1301–1312 (2006)
Maye, A., Engel, A.K.: A discrete computational model of sensorimotor contingencies for object perception and control of behavior. In: 2011 IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 3810–3815. IEEE (May 2011)
Maye, A., Engel, A.K.: Time Scales of Sensorimotor Contingencies. In: Zhang, H., Hussain, A., Liu, D., Wang, Z. (eds.) BICS 2012. LNCS (LNAI), vol. 7366, pp. 240–249. Springer, Heidelberg (2012)
Möller, R., Schenck, W.: Bootstrapping cognition from behavior – a computerized thought experiment. Cognitive Science 32(3), 504–542 (2008)
Noë, A.: Action in perception. MIT Press (2004)
O’Regan, J.K., Noë, A.: A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences 24, 939–1031 (2001)
Pfeifer, R., Scheier, C.: Sensory-motor coordination: The metaphor and beyond. Robotics and Autonomous Systems 20, 157–178 (1997)
Reinstein, M., Hoffmann, M.: Dead reckoning in a dynamic quadruped robot: Inertial navigation system aided by a legged odometer. In: 2011 IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 617–624 (2011)
Sirigu, A., Duhamel, J.R., Poncet, M.: The role of sensorimotor experience in object recognition. A case of multimodal agnosia. Brain: A Journal of Neurology 114(pt. 6), 2555–2573 (1991)
Ugur, E., Oztop, E., Sahin, E.: Goal emulation and planning in perceptual space using learned affordances. Robotics and Autonomous Systems 59, 580–595 (2011)
Ugur, E., Sahin, E.: Traversability: a case study for learning and perceiving affordances in robots. Adaptive Behavior 18, 258–284 (2010)
Webots. Commercial Mobile Robot Simulation Software, http://www.cyberbotics.com
Wolk, D.A., Coslett, H.B., Glosser, G.: The role of sensory-motor information in object recognition: Evidence from category-specific visual agnosia. Brain and Language 94(2), 131–146 (2005)
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Hoffmann, M., Schmidt, N.M., Pfeifer, R., Engel, A.K., Maye, A. (2012). Using Sensorimotor Contingencies for Terrain Discrimination and Adaptive Walking Behavior in the Quadruped Robot Puppy. In: Ziemke, T., Balkenius, C., Hallam, J. (eds) From Animals to Animats 12. SAB 2012. Lecture Notes in Computer Science(), vol 7426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33093-3_6
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DOI: https://doi.org/10.1007/978-3-642-33093-3_6
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