An Episodic Long-Term Memory for Robots: The Bender Case

  • María-Loreto SánchezEmail author
  • Mauricio Correa
  • Luz Martínez
  • Javier Ruiz-del-Solar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9513)


The main goal of this paper is to propose a framework for providing an episodic long-term memory for a robot, which includes methods for acquiring, storing, updating, managing and using episodic information. This will give a robot the ability to incorporate past experiences when interacting with humans, so that the data that the robot learns transcends each session, and thus gives continuity to its activities and behaviors. As a proof of concept, the implementation of an episodic long-term memory for the Bender robot is described. This includes the implementation and evaluation of a behavior called Conversation, which allows Bender to interact with people using the information stored in the episodic memory.


Service robots Human-robot interaction Episodic memory RoboCup@Home 



This work was partially funded by FONDECYT Project 1130153.


  1. 1.
    Wood, R., Baxter, P., Belpaeme, T.: A review of long-term memory in natural and synthetic systems. Adapt. Behav. 20(2), 81–103 (2012)CrossRefGoogle Scholar
  2. 2.
    Tulving, E.: Episodic and semantic memory. In: Donaldson, W. (ed.) Organization of Memory, pp. 381–403. Academic Press, New York (1972)Google Scholar
  3. 3.
    Bailey, C.H., Bartsch, D., Kandel, E.R.: Toward a molecular definition of long-term memory storage. Proc. Nat. Acad. Sci. USA 93(24), 13445–13452 (1996)CrossRefGoogle Scholar
  4. 4.
    Walker, M.P., Stickgold, R.: Sleep-dependent learning and memory consolidation. Neuron 44, 121–133 (2004)CrossRefGoogle Scholar
  5. 5.
    Nuxoll A., Laird, J. E.: A cognitive model of episodic memory integrated with a general cognitive architecture. In: Proceedings of 6th International Conference Cognition Modeling-ICCM 2004, Pittsburgh, PA, pp. 220–225 (2004)Google Scholar
  6. 6.
    Laird, J.E., Newell, A., Rosenbloom, P.S.: SOAR: an architecture for general intelligence. Artif. Intell. 33(1), 1–64 (1987)CrossRefGoogle Scholar
  7. 7.
    Ratanaswasd, P., Gordon, S., Dodd, W.: Cognitive control for robot task execution. In: IEEE International Workshop on Robot and Human Interactive Communication (RO-MAN), Nashville, Tennessee, 13–15 August (2005)Google Scholar
  8. 8.
    Dodd, W., Gutierrez, R.: The role of episodic memory and emotion in a cognitive robot. In: Proceedings of 14th Annual IEEE International Workshop on Robot and Human Interactive Communication (RO-MAN), pp. 692–697, Nashville, TN (2005)Google Scholar
  9. 9.
    Kuppuswamy, N.S., Cho, S., Kim, J.: A cognitive control architecture for an artificial creature using episodic memory. In: Proceedings of the 3rd SICE ICASE Internatioanl Joint Conference, pp. 3104–3110 (2006)Google Scholar
  10. 10.
    Jockel, S., Weser, M., Westhoff, D., Zhang, J.: Towards an episodic memory for cognitive robots. In: Proceedings of 6th Cognitive Robotics workshop at 18th European Conference on Artificial Intelligence (ECAI), pp. 68–74 (2008)Google Scholar
  11. 11.
    Deutsch, T., Gruber, A., Lang, R., Velik, R.: Episodic memory for autonomous agents. In: Proceedings of IEEE HSI Human System Interactions Conference, Krakow, Poland, 25–27 May (2008)Google Scholar
  12. 12.
    Spexard T.P., Siepmann F., Sagerer G.: Memory-based software integration for development in autonomous robotics. In: International Conference on Intelligent Autonomous Systems, pp. 49–53, Baden-Baden, Germany (2008)Google Scholar
  13. 13.
    Ho W.C., Lim M.Y., Vargas P.A., Enz S., Dautenhahn K., Aylett, R.: An initial memory model for virtual and robot companions supporting migration and long-term interaction. In: Proceedings of the 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 277–284. Toyama, Japan, September 27-October 2 (2009)Google Scholar
  14. 14.
    Winkler, J., Tenorth, M., Bozcuoglu, A., Beetz, M.: CRAMm - memories for robots performing everyday manipulation activities. In: Proceedings of the Second Annual Conference on Advances in Cognitive Systems, pp. 91–108 (2013)Google Scholar
  15. 15.
  16. 16.
    Roboearth.: World Wide Web for robots.
  17. 17.
    Martínez, L., Pavez, M., Olave, G., Correa, M., Sánchez, M.L., Loncomilla, P., Ruiz-del-Solar, J.: UChile homebreakers team description paper (2015).
  18. 18.
    Bender.: a Personal Robot.
  19. 19.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., Ng, A.: ROS: an open-source Robot Operating System. In: IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan (2009)Google Scholar
  20. 20.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 511–518 (2001)Google Scholar
  21. 21.
    Hadid, A., Ahonen, T., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  22. 22.
    Ruiz-del-Solar, J., Verschae, R., Correa, M.: Recognition of faces in unconstrained environments: a comparative study. EURASIP Journal on Advances in Signal Processing. Recent Advances in Biometric Systems, A Signal Processing Perspective) (2009)Google Scholar
  23. 23.
    Verschae, R., Ruiz-del-Solar, J., Correa, M.: A unified learning framework for object detection and classification using nested cascades of boosted classifiers. Mach. Vision Appl. 19(2), 85–103 (2008)CrossRefGoogle Scholar
  24. 24.
    Ruiz-del-Solar, J., Loncomilla, P.: Robot head pose detection and gaze direction determination using local invariant features. Adv. Robot. 23(3), 305–328 (2009)CrossRefGoogle Scholar
  25. 25.
    Martínez, L., Loncomilla, P., Ruiz-del-Solar, J.: Object recognition for manipulation tasks in real domestic settings: a comparative study. In: Bianchi, R.A.C., Akin, H.L., Ramamoorthy, S., Sugiura, K. (eds.) RoboCup 2014. LNCS, vol. 8992, pp. 207–219. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  26. 26.
    Speech recognition system pocketsphinx.
  27. 27.
    Speech synthesis system festival.
  28. 28.
    Open Source Database PostgreSQL.
  29. 29. sql\_database.
  30. 30.
  31. 31.
    Demonstrative video of the Conversation behavior.

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Authors and Affiliations

  • María-Loreto Sánchez
    • 1
    • 2
    Email author
  • Mauricio Correa
    • 1
    • 2
  • Luz Martínez
    • 1
    • 2
  • Javier Ruiz-del-Solar
    • 1
    • 2
  1. 1.Advanced Mining Technology CenterUniversidad de ChileSantiagoChile
  2. 2.Department of Electrical EngineeringUniversidad de ChileSantiagoChile

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