Cognitive Technical Systems — What Is the Role of Artificial Intelligence?

  • Michael Beetz
  • Martin Buss
  • Dirk Wollherr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4667)


The newly established cluster of excellence CoTeSys investigates the realization of cognitive capabilities such as perception, learning, reasoning, planning, and execution for technical systems including humanoid robots, flexible manufacturing systems, and autonomous vehicles. In this paper we describe cognitive technical systems using a sensor-equipped kitchen with a robotic assistant as an example. We will particularly consider the role of Artificial Intelligence in the research enterprise.

Key research foci of Artificial Intelligence research in CoTeSys include (∘) symbolic representations grounded in perception and action, (∘) first-order probabilistic representations of actions, objects, and situations, (∘) reasoning about objects and situations in the context of everyday manipulation tasks, and (∘) the representation and revision of robot plans for everyday activity.


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  1. 1.
    Buss, M., Beetz, M., Wollherr, D.: CoTeSys — cognition for technical systems. In: HAM. Proceedings of the 4th COE Workshop on Human Adaptive Mechatronics (2007)Google Scholar
  2. 2.
    Brachman, R.: Systems that know what they’re doing. IEEE Intelligent Systems, 67–71 (November/December 2002)Google Scholar
  3. 3.
    Zäh, M.F., Lau, C., Wiesbeck, M., Ostgathe, M., Vogl, W.: Towards the cognitive factory. In: CARV 2007. Proceedings of the 2nd International Conference on Changeable, Agile, Reconfigurable and Virtual Production (2007)Google Scholar
  4. 4.
    Ott, C., Eiberger, O., Friedl, W., Bauml, B., Hillenbrand, U., Borst, C., Albu-Schaffer, A., Brunner, B., Hirschmuller, H., Kielhofer, S., Konietschke, R., Suppa, M., Wimbock, T., Zacharias, F., Hirzinger, G.: A humanoid two-arm system for dexterous manipulation. In: Proceedings of the 6th IEEE-RAS International Conference on Humanoid Robots, IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  5. 5.
    Ott, C., Eiberger, O., Friedl, W., Bäuml, B., Hillenbrand, U., Borst, C., Albu-Schäffer, A., Brunner, B., Hirschmüller, H., Kielhöfer, S., Konietschke, R., Suppa, M., Wimböck, T., Zacharias, F., Hirzinger, G.: A humanoid two-arm system for dexterous manipulation. In: IEEE-RAS International Conference on Humanoid Robots, pp. 276–283. IEEE Computer Society Press, Los Alamitos (2006)CrossRefGoogle Scholar
  6. 6.
    Zacharias, F., Borst, C., Hirzinger, G.: Bridging the gap between task planning and path planning. In: Proceedings of IROS 2006. The IEEE International Conference on Intelligent Robots and Systems, Beijing, China, pp. 4490–4495. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  7. 7.
    Ulbrich, H., Buschmann, T., Lohmeier, S.: Development of the humanoid robot LOLA. Journal of Applied Mechanics and Materials 5(6), 529–539 (2006)CrossRefGoogle Scholar
  8. 8.
    Lohmeier, S., Buschmann, T., Ulbrich, H., Pfeiffer, F.: Modular joint design for a performance enhanced humanoid robot. In: ICRA. Proceedings of the 2006 IEEE Int. Conf. Rob. Aut., Orlando, USA, pp. 88–93. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  9. 9.
    Gienger, M., Löffler, K., Pfeiffer, F.: Design and realization of jogging Johnnie. In: CISM Courses and Lectures (2002)Google Scholar
  10. 10.
    Buss, M., Hardt, M., Kiener, J., Sobotka, M., Stelzer, M., von Stryk, O., Wollherr, D.: Towards an autonomous, humanoid, and dynamically walking robot: Modelling, optimal trajectory planning, hardware architecture, and experiments. In: Proceedings of the IEEE/RAS International Conference on Humanoid Robots, Karlsruhe, Germany (2003)Google Scholar
  11. 11.
    Wollherr, D., Buss, M., Hardt, M., von Stryk, O.: Research and development towards an autonomous biped walking robot. In: AIM 2003. Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Kobe, Japan, pp. 968–973 (2003)Google Scholar
  12. 12.
    Sobotka, M., Wollherr, D., Buss, M.: A jacobian method for online modification of precalculated gait trajectories. In: Proceedings of the 6th International Conference on Climbing and Walking Robots, Catania, Italy, pp. 435–442 (2003)Google Scholar
  13. 13.
    Beetz, M., Bandouch, J., Kirsch, A., Maldonado, A., Müller, A., Rusu, R.B.: The assistive kitchen — a demonstration scenario for cognitive technical systems. In: Proceedings of the 4th COE Workshop on Human Adaptive Mechatronics (HAM) (2007)Google Scholar
  14. 14.
    Doherty, P., Granlund, G., Kuchcinski, K., Sandewall, E., Nordberg, K., Skarman, E., Wiklund, J.: The WITAS unmanned aerial vehicle project. In: ECAI 2000. Proc. of the European Conference on Artificial Intelligence (2000)Google Scholar
  15. 15.
    Muscettola, N., Nayak, P.P., Pell, B., Williams, B.: Remote Agent: To boldly go where no AI system has gone before. Artificial Intelligence 103, 5–48 (1998)MATHCrossRefGoogle Scholar
  16. 16.
    Thrun, S., Beetz, M., Bennewitz, M., Cremers, A., Dellaert, F., Fox, D., Hähnel, D., Rosenberg, C., Roy, N., Schulte, J., Schulz, D.: Probabilistic algorithms and the interactive museum tour-guide robot Minerva. International Journal of Robotics Research (2000)Google Scholar
  17. 17.
    Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., Lau, K., Oakley, C., Palatucci, M., Pratt, V., Stang, P., Strohband, S., Dupont, C., Jendrossek, L.-E., Koelen, C., Markey, C., Rummel, C., Van Niekerk, J., Jensen, E., Alessandrini, P., Bradski, G., Davies, B., Ettinger, S., Kaehler, A., Nefian, A., Mahoney, P.: Winning the darpa grand challenge. Journal of Field Robotics, 2006 (accepted for publication)Google Scholar
  18. 18.
    Bertero, M., Poggio, T., Torre, V.: Ill-posed problems in early vision. Proceedings of the IEEE 76(8), 869–889 (1988)CrossRefGoogle Scholar
  19. 19.
    Koerding, K.P., Wolpert, D.M.: Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences 10, 319–326 (2006)CrossRefGoogle Scholar
  20. 20.
    Schaal, S., Schweighofer, N.: computational motor control in humans and robots. Current Opinion in Neurobiology (6), 675–682 (2005)Google Scholar
  21. 21.
    Chater, N., Tenenbaum, J.B., Yuille, A.: Probabilistic models of cognition: Conceptual foundations. Trends in Cognitive Sciences 10(7) (2006)Google Scholar
  22. 22.
    Chater, N., Tenenbaum, J.B., Yuille, A.: Probabilistic models of cognition: where next? Trends in Cognitive Sciences 10(7) (2006)Google Scholar
  23. 23.
    Gopnik, A., Glymour, C., Sobel, D., Schulz, L.: A theory of causal learning in children: causal maps and bayes nets. Psychological review (2004)Google Scholar
  24. 24.
    Gopnik, A., Glymour, C., Sobel, D., Schulz, L.: Causal learning mechanisms in very young children: two- three-, and four-year-olds infer causal relations from patterns of variation and covariation. Developmental Psychology 37 (2001)Google Scholar
  25. 25.
    Skaggs, W., McNaughton, B.: Spatial firing properties of hippocampal ca1 populations in an environment containing two visually identical regions. Journal of Neuroscience 18 (1998)Google Scholar
  26. 26.
    Dickmanns, E.: Dynamic Vision for Perception and Control of Motion. Springer, Heidelberg (2007)Google Scholar
  27. 27.
    A research roadmap of cognitive vision, Tech. Rep. v5, P Auer et al, ECVISION (2005),
  28. 28.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)MATHGoogle Scholar
  29. 29.
    Beetz, M., Kirchlechner, B., Lames, M.: Computerized real-time analysis of football games. IEEE Pervasive Computing 4(3), 33–39 (2005)CrossRefGoogle Scholar
  30. 30.
    Brown, L.E., Rosenbaum, D.A.: Encyclopedia of Cognitive Science. In: Motor Control: Models, Macmillan, London (2002)Google Scholar
  31. 31.
    Barnard, P., Dayan, P., Redgrave, P.: Foresight cognitive systems project research review: Action. tech. rep.Google Scholar
  32. 32.
    Buss, M.: Hybrid control of mechatronic systems. Systems, Control and Information 46(3), 129–137 (2002)MathSciNetGoogle Scholar
  33. 33.
    Reiter, R.: Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems. MIT Press, Cambridge (2001)MATHGoogle Scholar
  34. 34.
    Sutton, R., Barto, A.: Reinforcement Learning: an Introduction. MIT Press, Cambridge (1998)Google Scholar
  35. 35.
    Stulp, F., Beetz, M.: Optimized execution of action chains using learned performance models of abstract actions. In: IJCAI. Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (2005)Google Scholar
  36. 36.
    Morris, R., Hitch, G., Graham, K., Bussey, T.: Foresight cognitive systems project research review: Learning and memory. tech. rep.Google Scholar
  37. 37.
    Peters, J., Mistry, M., Udwadia, F., Cory, R., Nakanishi, J., Schaal, S.: A unifying methodology for the control of robotic systems. In: IROS. Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (2005)Google Scholar
  38. 38.
    Kawato, M.: Feedback-error-learning neural network for supervised motor learning. In: Advanced Neural Computers, pp. 365–472. Elsevier Science Publishers, Amsterdam (1990)Google Scholar
  39. 39.
    Alur, R., Courcoubetis, C., Henzinger, T.A., Ho, P.-H.: Hybrid automata: An algorithmic approach to the specification and verification of hybrid systems. In: Grossman, R.L., Ravn, A.P., Rischel, H., Nerode, A. (eds.) Hybrid Systems. LNCS, vol. 736, pp. 209–229. Springer, Heidelberg (1993)Google Scholar
  40. 40.
    Lenat, D.B.: Cyc: a large-scale investment in knowledge infrastructure. Communications of the ACM 38(11) (1995)Google Scholar
  41. 41.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American Magazine (May 2001)Google Scholar
  42. 42.
    Davis, E.: Representations of Commonsense Knowledge. Kaufmann Publishers Inc., San Francisco (1990)Google Scholar
  43. 43.
    Mitchell, T.M.: The Discipline of Machine Learning. Carnegie Mellon University, White Paper (2006)Google Scholar
  44. 44.
    Beetz, M.: A roadmap for research in robot planning. tech. rep., PLANET: European Network of Excellence in AI Planning (2004),
  45. 45.
    Cambon, S., Gravot, F., Alami, R.: A robot task planner that merges symbolic and geometric reasoning. In: ECAI 2004. 16th European Conference on Artificial Intelligence, pp. 895–899 (2004)Google Scholar
  46. 46.
    Kuffner, J., Nishiwaki, K., Kagami, S., Inaba, M., Inoue, H.: Motion planning for humanoid robots (2003)Google Scholar
  47. 47.
    Kuffner, J., LaValle, S.: RRT-connect: An efficient approach to single-query path planning. In: ICRA 2000. Proc. IEEE Int’l Conf. on Robotics and Automation, San Francisco, CA, April 2000, IEEE Computer Society Press, Los Alamitos (2000)Google Scholar
  48. 48.
    Kemp, C., Edsinger, A., Torres-Jara, E.: Challenges for robot manipulation in human environments. IEEE Robotics & Automation Magazine 14, 20–29 (2007)CrossRefGoogle Scholar
  49. 49.
    Rusu, R.B., Maldonado, A., Beetz, M., Gerkey, B.: Extending Player/Stage/Gazebo towards cognitive robots acting in ubiquitous sensor-equipped environments. In: Accepted for the IEEE International Conference on Robotics and Automation (ICRA) Workshop for Network Robot System, Rome, Italy, April 14, 2007, IEEE Computer Society Press, Los Alamitos (2007)Google Scholar
  50. 50.
    Patterson, D., Fox, D., Kautz, H., Philipose, M.: Fine-grained activity recognition by aggregating abstract object usage. In: Proceedings of the IEEE International Symposium on Wearable Computers, Osaka, Japan, October 2005, IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  51. 51.
    Philipose, M., Fishkin, K., Perkowitz, M., Patterson, D., Fox, D., Kautz, H., Hahnel, D.: Inferring activities from interactions with objects. Pervasive Computing, IEEE (2004)Google Scholar
  52. 52.
    Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder:real-time tracking of the human body. Tech. Rep. 353, MIT Media Lab (1996)Google Scholar
  53. 53.
    Smith, J., Fishkin, K., Jiang, B., Mamishev, A., Philipose, M., Rea, A., Roy, S., Sundara-Rajan, K.: Rfid-based techniques for human activity recognition. Communications of the ACM (September 2005)Google Scholar
  54. 54.
    Rusu, R.B., Blodow, N., Marton, Z., Soos, A., Beetz, M.: Towards 3d object maps for autonomous household robots. In: Submitted to Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS) (2007)Google Scholar
  55. 55.
    Domingos, P., Richardson, M.: Markov Logic: A Unifying Framework for Statistical Relational Learning. In: Proceedings of the ICML 2004 Workshop on Statistical Relational Learning and its Connections to Other Fields, pp. 49–54 (2004)Google Scholar
  56. 56.
    Domingos, P.: What’s Missing in AI: The Interface Layer. In: Cohen, P. (ed.) Artificial Intelligence: The First Hundred Years, AAAI Press (2006)Google Scholar
  57. 57.
    Hoyningen-Huene, N.v., Kirchlechner, B., Beetz, M.: GrAM: Reasoning with grounded action models by combining knowledge representation and data mining. In: Towards Affordance-based Robot Control. LNCS (LNAI), Springer, Heidelberg (to appear, 2007)Google Scholar
  58. 58.
    Bylander, T.: The computational complexity of propositional STRIPS planning. Artificial Intelligence 69(1-2), 165–204 (1994)MATHCrossRefMathSciNetGoogle Scholar
  59. 59.
    Fox, M., Long, D.: PDDL2.1: An extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research 20, 61–124 (2003)MATHGoogle Scholar
  60. 60.
    Beetz, M.: Plan representation for robotic agents. In: Proceedings of the Sixth International Conference on AI Planning and Scheduling, Menlo Park, CA, pp. 223–232. AAAI Press (2002)Google Scholar
  61. 61.
    Firby, R.J., Prokopowicz, P., Swain, M.: Plan representations for picking up trash. In: Proceedings of the Int. Joint Conf. on Artificial Intelligence (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Michael Beetz
    • 1
  • Martin Buss
    • 2
  • Dirk Wollherr
    • 2
  1. 1.Institute of Automatic Control Engineering (LSR), Faculty of Electrical Engineering and Information Technology 
  2. 2.Intelligent Autonomous Systems, Department of Informatics, Technische Universität München, D-80290 MünchenGermany

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