EXC 142:  Cognition for Technical Systems (CoTeSys)

Final Report Abstract

The COTESYS cluster of excellence investigated cognition for technical systems such as autonomous robots, manufacturing systems, and vehicles. Cognitive technical systems are equipped with technical sensors and actuators, integrated into physical systems, and act autonomously in the physical world. They differ from other technical systems as they perform cognitive control and have cognitive capabilities. Cognitive control orchestrates reflexive and habitual behavior in accordance with longterm autonomy and intentions. Cognitive capabilities include perception, reasoning, learning, goal-oriented planning, and result in systems of higher autonomy, flexibility, adaptivity, reliability, robustness and have better human interaction and collaboration capabilities. The scientific approach is based on (multiple) perception–cognition–action (PCA) closed loop systems including human-system joint cognition and collaborative action. The COTESYS cluster combined research competences in neuroscience, natural sciences, engineering, informatics, and humanities from the Technische Universität München (TUM), the Ludwig- Maximilians-Universität (LMU), the Universität der Bundeswehr (UBM), the Deutsches Zentrum für Luft- und Raumfahrt (DLR), and the Max-Planck-Institute for Neurobiology (MPI) to understand, model, analyze, and synthesize the information processing, decision making, and action mechanisms needed for cognition-enabled technical systems. Interdisciplinary research in neurobiology, neuro-cognition, cognitive science, and mathematics formed the basis for novel engineering and computing approaches to "artificial" cognition, which were validated by methods from psychology and ergonomics. The COTESYS research program was based on two components: the realization of cognitive capabilities and the focus on complex technical systems. The demonstration scenarios cognitionenabled kitchen, cognition-enabled factory, and cognition-enabled joint action were world-leading in their domains. New interdisciplinary and groundbreaking research results and methods were validated in these complex experimental systems. Because of these characteristics, COTESYS was in an excellent position to exploit Munich’s outstanding research infrastructure and strengths in an optimal way. Having technical systems as our targeted application fields positioned COTESYS in a key innovation field that is vital for Germany’s high-tech industry. The cooperation across disciplines and institutes produced synergies that will be kept and further strengthen the leadingedge of research, education, and technology in Munich even after the funding period of the cluster. The focus of the last funding period of the cluster was on the one hand to ensure the termination of as many PhD theses as possible and on the other hand the attraction of new third-party fundings.

Link to the final report

http://dx.doi.org/10.2314/GBV:880803711

Publications

• Learning local objective functions for robust face model fitting, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 30(8):1357-1370, 2008
  M. Wimmer, F. Stulp, S. Pietzsch & B. Radig
  
• Robots in the kitchen: Exploiting ubiquitous sensing and actuation, Robotics and Autonomous Systems Journal, Special Issue on Network Robot Systems, 56(10):844-856, 2008
  R.B. Rusu, B. Gerkey & M. Beetz
  
• Attentional selection of multiple goal positions before rapid hand movement sequences: an ERP study, Journal of Cognitive Neuroscience, 21(1):18-29, 2009
  D. Baldauf & H. Deubel
  
• Handing-over a cube: spatial features of physical joint action, Annals of the New York Academy of Sciences, 1164:380-382, 2009
  M. Huber, A. Knoll, T. Brandt and S. Glasauer
  
• Hydrodynamic object recognition: When multipoles count, Phys. Rev. Lett., 102(5), 058104, 2009
  A.B. Sichert, R. Bamler and J.L. van Hemmen
  
• Neural mechanisms of concurrent stimulus processing in dual tasks, Neuroimage, 48(1):237-248, 2009
  C. Stelzel, S. Brandt & T. Schubert
  
• Social cognitive neuroscience and humanoid robotics, Journal of Physiology - Paris, 103(3-5):286-295, 2009
  G. Cheng and T. Chaminade
  
• TMS evidence for smooth pursuit gain control by the frontal eye fields, Cerebral Cortex, 19:1144-50, 2009
  U. Nuding, R. Kalla, N.G. Muggleton, U. Büttner, V. Walsh & S. Glasauer
  
• Wake tracking and the detection of vortex rings by the canal lateral line of fish, Phys. Rev. Lett., 103, 078102, 2009
  J.-M.P. Franosch, H.J.A. Hagedorn, J. Goulet, J. Engelmann, and J.L. van Hemmen
  
• Advance planning in sequential pick-and-place tasks, Journal of Neurophysiology, 104(1):508-516, 2010
  C. Hesse & H. Deubel
  
• Electrophysiological correlates of detecting a visual target and detecting its absence: the role of feature dimensions, Neuropsychologia, 48:3365-3370, 2010
  E. Akyürek, A. Dinkelbach, A. Schubö & H. Müller
  
• How stimulus shape affects lateral-line perception: analytical approach to analyzing natural stimulus characteristics, Biological Cybernetics 102(3):177-180, 2010
  A.B. Sichert and J.L. van Hemmen
  
• Observing fearful faces leads to visuo-spatial perspective taking, Cognition, 117(1):101-105, 2010
  J. Zwickel & H.J. Müller
  
• ON and OFF pathways in Drosophila motion vision, Nature, 468:300-304, 2010
  M. Joesch, B. Schnell, V.R. Shamprasad, D.F. Reiff & A. Borst
  
• On the temporal relation of top-down and bottom-up mechanisms during guidance of attention, Journal of Cognitive Neuroscience, 22(4):640-654, 2010
  A. Wykowska & A. Schubö
  
• The impact of animal-like features on emotion expression of robot head EDDIE, Advanced Robotics, 24(8-9):1239-1255, 2010
  K. Kuehnlenz, S. Sosnowski & M. Buss
  
• Visualizing retinotopic half-wave rectified input to the motion detection circuitry of Drosophila, Nature Neuroscience, 13:973-978, 2010
  D.F. Reiff, J. Plett, M. Mank, O. Griesbeck & A. Borst
  
• Combined 2D-3D categorization and classification for multimodal perception systems, International Journal of Robotics Research, Vol. 30. 2011, Issue 11, pp. 1378-1402.
  Z.-C. Marton, D. Pangercic, N. Blodow and M. Beetz
  
• How to test for dual-task specific effects in brain imaging studies – An evaluation of potential analysis methods, Neuroimage, 54(3):1765- 1773, 2011
  A.J. Szameitat, T. Schubert & H.J. Müller
  
• Mobile visual location recognition, IEEE Signal Processing Magazine, Special Issue on Mobile Media Search, 28(4), 77-89, 2011
  G. Schroth, R. Huitl, D. Chen, M. Abu-Alqumsan, A. Al-Nuaimi, E. Steinbach
  
• Predictive remapping of attention across eye movements, Nature Neuroscience, 14(2):252-256, 2011
  M. Rolfs, D. Jonikaitis, H. Deubel & P. Cavanagh
  
• RoboEarth - A world wide web for robots. IEEE Robotics & Automation Magazine, Vol. 18. 2011, no. 2, pp. 69-82.
  M. Waibel, M. Beetz, R. D’Andrea, R. Janssen, M. Tenorth, J. Civera, J. Elfring, D. Gálvez- López, K. Häussermann, J.M.M. Montiel, A. Perzylo, B. Schießle, O. Zweigle & R. van de Molengraft
  
• System interdependence analysis for autonomous robots, The International Journal of Robotics Research, 30:601-614, 2011
  G. Lidoris, F. Rohrmüller, D. Wollherr & M. Buss
  
• Web-enabled Robots – Robots that use the Web as an Information Resource, Robotics & Automation Magazine, 18(2):58-68, 2011
  M. Tenorth, U. Klank, D. Pangercic & M. Beetz
  
• Cognition-enabled autonomous robot control for the realization of home chore task intelligence, Proceedings of the IEEE, Vol. 100. 2012, Issue 8, pp. 2454 - 2471.
  M. Beetz, D. Jain, L. Mösenlechner, L. Kunze, M. Tenorth, N. Blodow and D. Pangercic