Information System for Storage, Management, and Usage for Embodied Intelligent Systems

  • Daniel BeßlerEmail author
  • Asil Kaan Bozcuoğlu
  • Michael Beetz


Embodied intelligent agents that are equipped with sensors and actuators have unique characteristics and requirements regarding the storage, management, and usage of information. The goal is to perform intentional activities, within the perception-action loop of the agent, based on the information acquired from its senses, background knowledge, naive physics knowledge, etc. The challenge is to integrate many different types of information required for competent and intelligent decision-making into a coherent information system. In this chapter, we will describe a conceptual framework in which such information system can be represented and talked about. We will provide an overview about the different types of information an intelligent robot needs to adaptively and dexterously perform everyday activities. In our framework, every time a robot performs an activity, it creates an episodic memory. It can also acquire experiences from mental simulations, learn from these real and simulated experiences, and share them with other robots through dedicated knowledge web services.


  1. 1.
    Anderson, J.R., Schooler, L.J.: Reflections of the environment in memory. Psychol. Sci. 2(6), 396–408 (1991)CrossRefGoogle Scholar
  2. 2.
    Anderson, J.R., Greeno, J.G., Reder, L.M., Simon, H.A.: Perspectives on learning, thinking, and activity. Educ. Res. 29(4), 11–13 (2000)CrossRefGoogle Scholar
  3. 3.
    Anderson, J.R., Myowa-Yamakoshi, M., Matsuzawa, T.: Contagious yawning in chimpanzees. Proc. R. Soc. Lond. B: Biol. Sci. 271(Suppl. 6), S468–S470 (2004)Google Scholar
  4. 4.
    Bateman, J., Beetz, M., Beßler, D., Bozcuoglu, A.K., Pomarlan, M.: Heterogeneous ontologies and hybrid reasoning for service robotics: the ease framework. In: Third Iberian Robotics Conference, ROBOT’17, Sevilla (2017)Google Scholar
  5. 5.
    Beetz, M., Klank, U., Kresse, I., Maldonado, A., Mösenlechner, L., Pangercic, D., Rühr, T., Tenorth, M.: Robotic roommates making pancakes. In: 2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 529–536. IEEE, Piscataway (2011)Google Scholar
  6. 6.
    Beetz, M., Jain, D., Mösenlechner, L., Tenorth, M., Kunze, L., Blodow, N., Pangercic, D.: Cognition-enabled autonomous robot control for the realization of home chore task intelligence. Proc. IEEE 100(8), 2454–2471 (2012)CrossRefGoogle Scholar
  7. 7.
    Beetz, M., Tenorth, M., Winkler, J.: Open-EASE – a knowledge processing service for robots and robotics/AI researchers. In: IEEE International Conference on Robotics and Automation (ICRA), Seattle (2015)Google Scholar
  8. 8.
    Beetz, M., Beßler, D., Haidu, A., Pomarlan, M., Bozcuoglu, A.K., Bartels, G.: Knowrob 2.0 – a 2nd generation knowledge processing framework for cognition-enabled robotic agents. In: International Conference on Robotics and Automation (ICRA), Brisbane (2018)Google Scholar
  9. 9.
    Benbrahim, H., Franklin, J.A.: Biped dynamic walking using reinforcement learning. Robot. Auton. Syst. 22(3–4), 283–302 (1997)CrossRefGoogle Scholar
  10. 10.
    Bozcuoglu, A.K., Beetz, M.: A cloud service for robotic mental simulations. In: International Conference on Robotics and Automation (ICRA), Singapore (2017)Google Scholar
  11. 11.
    Bozcuoglu, A.K., Kazhoyan, G., Furuta, Y., Stelter, S., Beetz, M., Okada, K., Inaba, M.: The exchange of knowledge using cloud robotics. Robot. Autom. Lett. 3(2), 1072–1079 (2018)CrossRefGoogle Scholar
  12. 12.
    Brachman, R.J.: Systems that know what they’re doing. IEEE Intell. Syst. 17(6), 67–71 (2002)CrossRefGoogle Scholar
  13. 13.
    Davis, E.: A logical framework for solid object physics. Tech. Rep. 245 (1986)Google Scholar
  14. 14.
    Duan, Y., Andrychowicz, M., Stadie, B., Ho, O.J., Schneider, J., Sutskever, I., Abbeel, P., Zaremba, W.: One-shot imitation learning. In: Advances in Neural Information Processing Systems, pp. 1087–1098 (2017)Google Scholar
  15. 15.
    Fox, M., Long, D.: PDDL2.1: an extension of PDDL for expressing temporal planning domains. J. Artif. Intell. Res. 20, 61–124 (2003)CrossRefGoogle Scholar
  16. 16.
    Haidu, A., Beßler, D., Bozcuoglu, A.K., Beetz, M.: Knowrob-sim – game engine-enabled knowledge processing for cognition-enabled robot control. In: International Conference on Intelligent Robots and Systems (IROS). IEEE, Madrid (2018)Google Scholar
  17. 17.
    Hegarty, M.: Mechanical reasoning by mental simulation. Trends Cogn. Sci. 8(6), 280–285 (2004)CrossRefGoogle Scholar
  18. 18.
    Ishai, A., Haxby, J.V., Ungerleider, L.G.: Visual imagery of famous faces: effects of memory and attention revealed by fMRI. Neuroimage 17(4), 1729–1741 (2002)CrossRefGoogle Scholar
  19. 19.
    Kaelbling, L.P., Littman, M.L., Cassandra, A.R.: Planning and acting in partially observable stochastic domains. Artif. Intell. 101(1–2), 99–134 (1998)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Kim, B.: Interactive and interpretable machine learning models for human machine collaboration. Ph.D. Thesis, Massachusetts Institute of Technology (2015)Google Scholar
  21. 21.
    Kim, H.J., Jordan, M.I., Sastry, S., Ng, A.Y.: Autonomous helicopter flight via reinforcement learning. In: Advances in Neural Information Processing Systems, pp. 799–806 (2004)Google Scholar
  22. 22.
    Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps. Int. J. Robot. Res. 34(4–5), 705–724 (2015)CrossRefGoogle Scholar
  23. 23.
    Lisca, G., Nyga, D., Bálint-Benczédi, F., Langer, H., Beetz, M.: Towards robots conducting chemical experiments. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5202–5208. IEEE, Piscataway (2015)Google Scholar
  24. 24.
    McDermott, D.: The formal semantics of processes in PDDL. In: Proceedings of the ICAPS Workshop on PDDL (2003)Google Scholar
  25. 25.
    McLaughlin, B.: “Intentional” and “incidental” learning in human subjects: the role of instructions to learn and motivation. Psychol. Bull. 63(5), 359 (1965)CrossRefGoogle Scholar
  26. 26.
    Morgenstern, L.: Mid-sized axiomatizations of commonsense problems: a case study in egg cracking. Stud. Logica 67(3), 333–384 (2001)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Mösenlechner, L., Beetz, M.: Parameterizing actions to have the appropriate effects. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Francisco (2011)Google Scholar
  28. 28.
    Okada, K., Kino, Y., Inaba, M., Inoue, H.: Visually-based humanoid remote control system under operator’s assistance and its application to object manipulation. In: Proceedings of Third IEEE International Conference on Humanoid Robots (2003)Google Scholar
  29. 29.
    Ratey, J.J., Galaburda, A.M.: A User’s Guide to the Brain: Perception, Attention, and the Four Theaters of the Brain. Vintage Series. Vintage Books, New York (2002)Google Scholar
  30. 30.
    Reiser, U., Connette, C., Fischer, J., Kubacki, J., Bubeck, A., Weisshardt, F., Jacobs, T., Parlitz, C., Hägele, M., Verl, A.: Care-o-bot® 3-creating a product vision for service robot applications by integrating design and technology. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009 (IROS 2009), pp. 1992–1998. IEEE, Piscataway (2009)Google Scholar
  31. 31.
    Reiter, R.: Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems. MIT Press, Cambridge (2001)CrossRefGoogle Scholar
  32. 32.
    Sakagami, Y., Watanabe, R., Aoyama, C., Matsunaga, S., Higaki, N., Fujimura, K.: The intelligent asimo: system overview and integration. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 2478–2483 (2002)Google Scholar
  33. 33.
    Saxena, A., Driemeyer, J., Ng, A.Y.: Robotic grasping of novel objects using vision. Int. J. Robot. Res. 27(2), 157–173 (2008)CrossRefGoogle Scholar
  34. 34.
    Shanahan, M.: A logical formalisation of Ernie Davis’s egg cracking problem. In: Problem Fourth Symposium on Logical Formalizations of Commonsense Reasoning (1997)Google Scholar
  35. 35.
    Siciliano, B., Khatib, O. (eds.): Springer Handbook of Robotics. Springer, Berlin (2008)zbMATHGoogle Scholar
  36. 36.
    Siskind, J.: Reconstructing force-dynamic models from video sequences. Artif. Intell. 151(1), 91–154 (2003)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Tenorth, M., Profanter, S., Balint-Benczedi, F., Beetz, M.: Decomposing CAD models of objects of daily use and reasoning about their functional parts. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo Big Sight, pp. 5943–5949 (2013)Google Scholar
  38. 38.
    Tenorth, M., Winkler, J., Beßler, D., Beetz, M.: Open-ease – a cloud-based knowledge service for autonomous learning. KI – Künstliche Intelligenz (2015)CrossRefGoogle Scholar
  39. 39.
    Tulving, E.: Episodic memory: from mind to brain. Annu. Rev. Psychol. 53(1), 1–25 (2002)CrossRefGoogle Scholar
  40. 40.
    Wyrobek, K.A., Berger, E.H., der Loos, H.F.M.V., Salisbury, J.K.: Towards a personal robotics development platform: rationale and design of an intrinsically safe personal robot. In: 2008 IEEE International Conference on Robotics and Automation, pp. 2165–2170 (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Daniel Beßler
    • 1
    • 2
    Email author
  • Asil Kaan Bozcuoğlu
    • 3
  • Michael Beetz
    • 3
  1. 1.Collaborative Research Centre “Everyday Activities Science and Engineering” (EASE)University of BremenBremenGermany
  2. 2.Institute for Artificial IntelligenceUniversity of BremenBremenGermany
  3. 3.Collaborative Research Centre “Everyday Activities Science and Engineering” (EASE)University of BremenBremenGermany

Personalised recommendations