Abstract
Brain-like robotic approaches aim to reproduce the complex processes occurring within the biological brains to achieve a higher level of autonomy. One of the key aspects of these approaches is dynamic learning, that is, how to provide the cognitive architectures that control de robot with adaptive learning capabilities. Several options have been considered in this line in the field of Cognitive Robotics, although the development of a proper memory system has provided the best practical results up to now. This work also follows this approach, seeking to show the advantages of using a Long-Term Memory (LTM) for optimizing the adaptive learning capabilities of a cognitive robot in dynamic environments. Specifically, a procedural LTM that stores basic models and behaviours is included in the evolutionary-based Multilevel Darwinist Brain (MDB) cognitive architecture. The LTM management system that has been developed to control when a model must be stored or replaced is presented here in detail. Moreover, a Short-Term Memory (STM) sub-system included in the MDB is also explained due to its strong relationship with the operation of the LTM. The LTM elements are tested in theoretical functions and in a simulated example using the AIBO robot in a dynamic context with successful adaptive learning results.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Asada M, MacDorman KF, Ishiguro H, Kuniyoshi Y (2001) Cognitive developmental robotics as a new paradigm for the design of humanoid robots. Robot Auton Syst 37:185–193
Atkinson R, Shiffrin R (1968) Human memory: a proposed system and its control processes. In: Spence KW, Spence JT (eds) The psychology of learning and motivation, vol 2. Academic Press, New York, pp 89–195
Bach J (2009) Principles of synthetic intelligence PSI: an architecture of motivated cognition, 1st edn. Oxford University Press, Inc, New York
Baddeley AD, Hitch G (1974) Working memory. In: Bower GH (ed) The psychology of learning and motivation: advances in research and theory, vol 8. Academic Press, New York, pp 47–89
Bellas F, Duro RJ, Faiña A, Souto D (2010) MDB: artificial evolution in a cognitive architecture for real robots. IEEE Transactions on autonomous mental development, vol 2. IEEE Press, pp 340–354
Cowan N (2008) What are the differences between long-term, short-term, and working memory? Prog Brain Res 169:323–338
Dayoub F, Duckett T, Cielniak G (2010) Toward an object-based semantic memory for long-term operation of mobile service robots. In: Workshop on semantic mapping and autonomous knowledge acquisition, IROS, Taipei
de Castro EC, Gudwin RR (2010) An episodic memory for a simulated autonomous robot. In: Proceedings of robocontrol, pp 1–7
Franklin S (2005) Cognitive robots: perceptual associative memory and learning. In: IEEE international workshop on robot and human interactive communication (ROMAN), pp 427–433
Goertzel B, de Garis H (2008) XIA-MAN: an extensible, integrative architecture for intelligent humanoid robotics. In: Proceedings of the BICA-08, pp 86–90
Goertzel B, Lian R, Arel I, de Garis H, Chen S (2010) A world survey of artificial brain projects, Part II: biologically inspired cognitive architectures. Neurocomputing 74(1–3):30–49
Jockel S, Weser M, Westhoff D, Zhang J (2008) Towards an episodic memory for cognitive robots. In: Proceedings of the 6th international cognitive robotics workshop at ECAI08. IOS Press, Amsterdam, pp 68–74. http://cinacs.informatik.uni-hamburg.de/cinacs-publications
Kandel ER, Schwartz JH, Jessell TM (2000) Principles of neural science, 4th edn. McGraw-Hill, New York
Kasabov N, Benuskova L, Wysoski S (2005) Biologically plausible computational neurogenetic models: modeling the interaction between genes/proteins, neurons and neural networks. J Comput Theoret Nanosci 2(4):569–575
Kawamura K, Gordon S, Ratanaswasd P, Erdemir E, Hall J (2008) Implementation of cognitive control for a humanoid robot. Int J Humanoid Rob 5(4):547–586
Krichmar JL, Edelman GM (2006) Principles underlying the construction of brain-based devices. In: Proceedings of AISB, vol 2, pp 37–42
Laird JE (2008) Extending the soar cognitive architecture. In: Proceeding of the 2008 conference on artificial general intelligence. IOS Press, Amsterdam, pp 224–235
Santos-Diez B, Bellas F, Faiña A, Duro RJ (2010) Lifelong learning by evolution in robotics: bridging the gap from theory to reality. In: Proceedings EAIS, pp 48–53
Solms M, Turnbull O (2002) The brain and the inner world. Karnac/Other Press, Cathy Miller Foreign Rights Agency, London
van Heuveln B (2010) What is cognitive robotics? http://www.cogsci.rpi.edu/~heuveb, Cognitive Science Department, Rensselaer Polytechnic Institute
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Bellas, F., Caamaño, P., Faiña, A. et al. Dynamic learning in cognitive robotics through a procedural long term memory. Evolving Systems 5, 49–63 (2014). https://doi.org/10.1007/s12530-013-9079-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12530-013-9079-4