A Unified Internal Representation of the Outer World for Social Robotics

  • Pablo Bustos
  • Luis J. Manso
  • Juan P. Bandera
  • Adrián Romero-Garcés
  • Luis V. Calderita
  • Rebeca Marfil
  • Antonio Bandera
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 418)


Enabling autonomous mobile manipulators to collaborate with people is a challenging research field with a wide range of applications. Collaboration means working with a partner to reach a common goal and it involves performing both, individual and joint actions, with her. Human-robot collaboration requires, at least, two conditions to be efficient: a) a common plan, usually under-defined, for all involved partners; and b) for each partner, the capability to infer the intentions of the other in order to coordinate the common behavior. This is a hard problem for robotics since people can change their minds on their envisaged goal or interrupt a task without delivering legible reasons. Also, collaborative robots should select their actions taking into account human-aware factors such as safety, reliability and comfort. Current robotic cognitive systems are usually limited in this respect as they lack the rich dynamic representations and the flexible human-aware planning capabilities needed to succeed in these collaboration tasks. In this paper, we address this problem by proposing and discussing a deep hybrid representation, DSR, which will be geometrically ordered at several layers of abstraction (deep) and will merge symbolic and geometric information (hybrid). This representation is part of a new agents-based robotics cognitive architecture called CORTEX. The agents that form part of CORTEX are in charge of high-level functionalities, reactive and deliberative, and share this representation among them. They keep it synchronized with the real world through sensor readings, and coherent with the internal domain knowledge by validating each update.


Social robotics World internalization Deep representations 


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  1. 1.
    Foote, T.: tf: The transform library. In: 2013 IEEE International Conference on Technologies for Practical Robot Applications (TePRA). Open-Source Software Workshop, pp. 1–6 (2013)Google Scholar
  2. 2.
    Blumenthal, S., Bruyninckx, H., Nowak, W., Prassler, E.: A scene graph based shared 3d world model for robotic applications. In: 2013 IEEE International Conference on Robotics and Automation, pp. 453–460, May 2013Google Scholar
  3. 3.
    Bustos, P., Martinez-Gomez, J., Garcia-Varea, I., Rodriguez-Ruiz, L., Bachiller, P., Calderita, L., Manso, L., Sanchez, A., Bandera, A., Bandera, J.: Multimodal interaction with loki. In: Workshop of Physical Agents, pp. 1–8 (2013)Google Scholar
  4. 4.
    Rusell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Pearson (2009)Google Scholar
  5. 5.
    Poole, D., Mackworth, A.: Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press (2010)Google Scholar
  6. 6.
    Manso, L.J.: Perception as stochastic sampling on dynamic graph spaces, Ph.D. dissertation (2013)Google Scholar
  7. 7.
    Calderita, L.V., Bustos, P., Suárez Mejías, C., Fernández, F., Bandera, A.: Therapist: towards an autonomous socially interactive robot for motor and neurorehabilitation therapies for children. In: 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, vol. 1, pp. 374–377 (2013)Google Scholar
  8. 8.
    Selfridge, O.G.: Pandamonium: a paradigm for learning. In: Proceedings of the Symposium on the Mechanization of Thought Processes, pp. 511–529 (1959)Google Scholar
  9. 9.
    Newell, A.: Some problems of basic organization in problem-solving programs, Tech. Rep. (1962)Google Scholar
  10. 10.
    Erman, L.D., Hayes-Roth, F., Lesser, V.R., Reddy, D.R.: The hearsay-ii speech-understanding system: Integrating knowledge to resolve uncertainty. ACM Computing Surveys 12(2), 213–253 (1980)CrossRefGoogle Scholar
  11. 11.
    Hayes-Roth, B.: A blackboard architecture for control. Artificial Intelligence1 26(2), 251–321 (1985)CrossRefGoogle Scholar
  12. 12.
    McManus, J.W.: Design and analysis of concurrent blackboard systems, Ph.D. dissertation (1992)Google Scholar
  13. 13.
    Corkill, D.D.: Blackboard systems. AI Expert 6(9) (1991)Google Scholar
  14. 14.
    Shapiro, M., Preguiça, N., Baquero, C., Zawirski, M.: Convergent and commutative replicated data types. Bulletin of the European (104) (2011)Google Scholar
  15. 15.
    Balegas, V., Ferreira, C., Rodrigues, R., Shapiro, M., Preguic, N., Najafzadeh, M.: Putting consistency back into eventual consistency. In: EuroSys 2015 (2015)Google Scholar
  16. 16.
    Pritchett, D.: Base: An acid alternative. Queue, June 2008Google Scholar
  17. 17.
    Naef, M., Lamboray, E., Staadt, O., Gross, M.: The blue-c distributed scene graph. In: Proceedings - Virtual Reality Annual International Symposium, pp. 275–276 (2003)Google Scholar
  18. 18.
    Lemaignan, S., Ros, R., Mösenlechner, L., Alami, R., Beetz, M.: Oro, a knowledge management platform for cognitive architectures in robotics. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3548–3553, October 2010Google Scholar
  19. 19.
    Hofland, K., Jørgensen, A.M., Drange, E., Stenström, A.: A Spanish spoken corpus of youth language. Corpus Linguistics (2005)Google Scholar
  20. 20.
    Jelinek, F.: Statistical methods for speech recognition. MIT Press (1997)Google Scholar
  21. 21.
    Chamorro, D., Vazquez Martin, R.: R-ORM: relajación en el método de evitar colisiones basado en restricciones. In: X Workshop de Agentes Físicos, Cáceres, España (2009)Google Scholar
  22. 22.
    Calderita, L.V., Manso, L.J., Bustos, P., Suárez-Mejías, C., Fernández, F., Bandera, A.: THERAPIST: Towards an Autonomous Socially Interactive Robot for Motor and Neurorehabilitation Therapies for Children. JMIR Rehabil. Assist. Technol. (2014)Google Scholar
  23. 23.
    Romero-Garcés, A., Calderita, L.V., González, J., Bandera, J.P., Marfil, R., Manso, L.J., Bandera, A., Bustos, P.: Testing a fully autonomous robotic salesman in real scenarios. In: Conference: IEEE International Conference on Autonomous Robot Systems and Competitions (2015)Google Scholar
  24. 24.
    Manso, L.J.: Perception as stochastic sampling on dynamic graph spaces, Ph.D. dissertation, Univ. of Extremadura, Spain (2013)Google Scholar
  25. 25.
    Tomasello, M., Carpenter, M., Call, J., Behne, T., Moll, H.: Understanding and sharing intentions: The origins of cultural cognition. Behavioral and Brain Sciences 28(5), 675–691 (2005)Google Scholar
  26. 26.
    Bauer, A., Wollherr, D., Buss, M.: Human-robot collaboration: a survey. Int. Journal of Humanoid Robotics (2007)Google Scholar
  27. 27.
    Kirsch, A., Kruse, T., Mösenlechner, L.: An integrated planning and learning framework for human-robot interaction. In: 4th Workshop on Planning and Plan Execution for Real-World Systems (held in conjunction with ICAPS 2009) (2009)Google Scholar
  28. 28.
    Beetz, M., Jain, D., Mösenlechner, L., Tenorth, M.: Towards performing everyday manipulation activities. Robotics and Autonomous Systems (2010)Google Scholar
  29. 29.
    Alami, R., Chatila, R., Clodic, A., Fleury, S., Herrb, M., Montreuil, V., Sisbot, E.A.: Towards human-aware cognitive robots. In: AAAI 2006, Stanford Spring Symposium (2006)Google Scholar
  30. 30.
    Wintermute, S.: Imagery in cognitive architecture: Representation and control at multiple levels of abstraction. Cognitive Systems Research 19–20, 1–29 (2012)CrossRefGoogle Scholar
  31. 31.
    Ali, M.: Contribution to decisional human-robot interaction: towards collaborative robot companions, PhD Thesis, Institut National de Sciences Appliquées de Toulouse, France (2012)Google Scholar
  32. 32.
    Clark, A.: An embodied cognitive science? Trends in Cognitive Sciences 3(9) (1999)Google Scholar
  33. 33.
    Holland, O.: The future of embodied artificial intelligence: machine consciousness? In: Embodied Artificial Intelligence, pp. 37–53 (2004)Google Scholar
  34. 34.
    Manso, L.J.: Perception as Stochastic Sampling on Dynamic Graph Spaces, PhD Thesis, University of Extremadura, Spain (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Pablo Bustos
    • 1
  • Luis J. Manso
    • 1
  • Juan P. Bandera
    • 2
  • Adrián Romero-Garcés
    • 2
  • Luis V. Calderita
    • 1
    • 2
  • Rebeca Marfil
    • 2
  • Antonio Bandera
    • 2
  1. 1.RoboLab GroupUniversity of ExtremaduraCáceresSpain
  2. 2.Dept. Tecnología ElectrónicaUniversity of MalagaMálagaSpain

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