On Cognitive Reasoning for Compliant Manipulation Tasks in Smart Production Environments

  • Daniel LeidnerEmail author
Dissertation and Habilitation Abstracts


Highly automated smart production environments require robots with autonomous planning mechanisms as well as effect-based performance inference methods. This report discusses the possibilities of cognitive reasoning for compliant manipulation tasks to satisfy these demands. The article builds on the representations for compliant wiping actions and their effects which are fundamental to many tasks in industrial manufacturing. It is described how these actions can be planned, executed, and interpreted by means of generic action descriptions and qualitative models.


Cognitive reasoning Physical compliance Smart production 



I would like to thank euRobotics AISBL for awarding the presented dissertation with the Georges Giralt PhD Award 2018 and the Helmholtz Association for awarding me with the Doctoral Prize 2018 in the research field Aeronautics, Space, and Transport. This work was supported by the German Research Foundation (DFG) within the Collaborative Research Center EASE (SFB 1320) and by the Bavarian Ministry of Economic Affairs and Media, Energy and Technology within the SMiLE Project (grant LABAY97).


  1. 1.
    Beetz M, Tenorth M, Winkler J (2015) Open-EASE—a knowledge processing service for robots and robotics/AI researchers. In: IEEE international conference on robotics and automation (ICRA), pp 1983–1990Google Scholar
  2. 2.
    Borst C, Wimböck T, Schmidt F, Fuchs M, Brunner B, Zacharias F, Giordano PR, Konietschke R, Sepp W, Fuchs S, et al (2009) Rollin’Justin—mobile platform with variable base. In: Proc. of the IEEE international conference on robotics and automation (ICRA), pp 1597–1598Google Scholar
  3. 3.
    Dietrich A, Wimböck T, Albu-Schäffer A, Hirzinger G (2012) Reactive whole-body control: dynamic mobile manipulation using a large number of actuated degrees of freedom. IEEE Robot Autom Mag 19(2):20–33CrossRefGoogle Scholar
  4. 4.
    Do M, Schill J, Ernesti J, Asfour T (2014) Learn to wipe: a case study of structural bootstrapping from sensorimotor experience. In: Proc. of the IEEE international conference on robotics and automation (ICRA), pp 1858–1864Google Scholar
  5. 5.
    Ghallab M, Howe A, Christianson D, McDermott D, Ram A, Veloso M, Weld D, Wilkins D (1998) PDDL—the planning domain definition language. AIPS98 Plan Comm 78(4):1–27Google Scholar
  6. 6.
    Gibson JJ (2014) The ecological approach to visual perception: classic edition. Psychology PressGoogle Scholar
  7. 7.
    Hess JM, Tipaldi GD, Burgard W (2012) Null Space Optimization for Effective Coverage of 3D Surfaces using redundant manipulators. In: Proc. of the IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 1923–1928Google Scholar
  8. 8.
    Leidner D (2017) Cognitive reasoning for compliant robot manipulation. Ph.D. thesis, University of BremenGoogle Scholar
  9. 9.
    Martínez D, Alenya G, Torras C (2015) Planning robot manipulation to clean planar surfaces. Eng Appl Artif Intell 39:23–32CrossRefGoogle Scholar
  10. 10.
    Norman DA, Shallice T (1980) Attention to action: willed and automatic control of behavior. Tech. rep., DTIC DocumentGoogle Scholar
  11. 11.
    Stelter S, Bartels G, Beetz M (2018) Multidimensional time-series shapelets reliably detect and classify contact events in force measurements of wiping actions. IEEE Robot Autom Lett 3(1):320–327CrossRefGoogle Scholar

Copyright information

© Gesellschaft für Informatik e.V. and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Deutsches Zentrum fur Luft- und Raumfahrt Institut fur Robotik und MechatronikWesslingGermany

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