An Optimization-Based Estimation and Adaptive Control Approach for Human-Robot Cooperation

  • Wilm DecréEmail author
  • Herman Bruyninckx
  • Joris De Schutter
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)


This paper presents a novel robot programming approach for actively assisting humans in human-robot cooperation tasks. First, the paper discusses an invariant description-based parametric modeling approach for six degree-of-freedom motion trajectories. This generic approach facilitates building a library of motion models in a systematic way. Second, the paper presents a constrained optimization-based parameter estimation technique for estimating the motion model parameters. Both batch and recursive schemes are presented. Third, the paper presents a control architecture based on our constraint-based task specification approach iTaSC that supports including secondary task objectives or inequality constraints (for example joint limits) in the robot task definition. The control architecture is exemplified using the KUKA LWR 4 robot and Orocos robot control software. Experimental results clearly indicate the potential of the approach by showing significant lower human-robot interaction forces compared to classical admittance control.


Motion Trajectory Control Architecture Feedforward Control Iterative Learning Control Robot Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aarno, D., Kragic, D.: Layered HMM for motion intention recognition. In: Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006, Beijing, China, pp. 5130–5135 (2006)Google Scholar
  2. 2.
    Aarno, D., Kragic, D.: Motion intention recognition in robot assisted applications. Robotics and Autonomous Systems 56, 692–705 (2008)CrossRefGoogle Scholar
  3. 3.
    Albu-Schäffer, A., Haddadin, S., Ott, C., Stemmer, A., Wimböck, T., Hirzinger, G.: The DLR lightweight robot: design and control concepts for robots in human environments. Industrial Robot: An International Journal 34(5), 376–385 (2007)CrossRefGoogle Scholar
  4. 4.
    Bruyninckx, H.: Open RObot COntrol Software (2001), (last visited 2010)
  5. 5.
    Corteville, B., Aertbeliën, E., Bruyninckx, H., De Schutter, J., Van Brussel, H.: Human-inspired robot assistant for fast point-to-point movements. In: Proceedings of the 2007 IEEE International Conference on Robotics and Automation, ICRA 2007, Rome, Italy, pp. 3639–3644 (2007)Google Scholar
  6. 6.
    Cox, H.: On the estimation of state variables and parameters for noisy dynamic systems. IEEE Transactions on Automatic Control 9(1), 5–12 (1964)CrossRefGoogle Scholar
  7. 7.
    Craig, J.J.: Introduction to Robotics: Mechanics and Control, 3rd edn. Prentice-Hall (2004)Google Scholar
  8. 8.
    De Schutter, J.: Invariant description of rigid body motion trajectories. Transactions of the ASME, Journal of Mechanisms and Robotics 2(1), 011004/1–9 (2010)Google Scholar
  9. 9.
    De Schutter, J., De Laet, T., Rutgeerts, J., Decré, W., Smits, R., Aertbeliën, E., Claes, K., Bruyninckx, H.: Constraint-based task specification and estimation for sensor-based robot systems in the presence of geometric uncertainty. The International Journal of Robotics Research 26(5), 433–455 (2007)CrossRefGoogle Scholar
  10. 10.
    Decré, W., Smits, R., Bruyninckx, H., De Schutter, J.: Extending iTaSC to support inequality constraints and non-instantaneous task specification. In: Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan, pp. 964–971 (2009)Google Scholar
  11. 11.
    Flash, T., Hogan, W.: The coordination of arm movements: an experimentally confirmed mathematical model. The Journal of Neuroscience 5(7), 1688–1703 (1985)Google Scholar
  12. 12.
    Gillespie, R.B., Colgate, J.E.: A survey of multibody dynamics for virtual environments. In: ASME International Mechanical Engineering Congress and Exhibition, Dallas, TX, pp. 45–54 (1997)Google Scholar
  13. 13.
    Gordon, N., Salmond, D.J., Smith, A.F.M.: Novel approach to nonlinear/non-Gaussian state estimation. IEE Proceedings-F 140(2), 107–113 (1993)Google Scholar
  14. 14.
    Hocoma, (accessed online on October 15, 2010)
  15. 15.
    Hogan, N.: Impedance control: An approach to manipulation. Parts I-III. Transactions of the ASME, Journal of Dynamic Systems, Measurement, and Control 107, 1–24 (1985)zbMATHCrossRefGoogle Scholar
  16. 16.
    Houska, B., Ferreau, H.J., Diehl, M.: Acado - an open-source toolkit for automatic control and dynamic optimization. In: Proceedings of the 2009 Belgian-French-German Conference on Optimization, Leuven, Belgium, p. 167 (2009)Google Scholar
  17. 17.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. Transactions of the ASME, Journal of Basic Engineering 82, 34–45 (1960)Google Scholar
  18. 18.
    Maeda, Y., Hara, T., Arai, T.: Cooperative human-robot handling of an object with motion estimation. Journal of Robotics and Mechatronics 14(5), 432–438 (2002)Google Scholar
  19. 19.
    Miller, B.E., Colgate, J.E., Freeman, R.A.: Guaranteed stability of haptic systems with nonlinear virtual environments. IEEE Transactions on Robotics and Automation 16(6), 712–719 (2000)CrossRefGoogle Scholar
  20. 20.
    Nocedal, J., Wright, S.J.: Numerical Optimization. Series in Operations Research. Springer (1999)Google Scholar
  21. 21.
    Schreiber, G., Stemmer, A., Bischoff, R.: The fast research interface for the kuka lightweight robot. In: IEEE Workshop on Innovative Robot Control Architectures for Demanding (Research) Applications How to Modify and Enhance Commercial Controllers, ICRA 2010 (May 2010)Google Scholar
  22. 22.
    Siciliano, B., Khatib, O.E.: Springer Handbook of Robotics. Springer, Heidelberg (2008)zbMATHCrossRefGoogle Scholar
  23. 23.
    Spong, M., Hutchinson, S., Vidyasagar, M.: Robot Modeling and Control, 2nd edn. John Wiley & Sons (2006)Google Scholar
  24. 24.
    Takeo, K.K., Fukuda, T.: Unified approach for teleoperation of virtual and real environment: Manipulation based on reference dynamics. In: Proceedings of the 1995 IEEE International Conference on Robotics and Automation, ICRA 1995, Nagoya, Japan, pp. 938–943 (1995)Google Scholar
  25. 25.
    van der Linde, R., Lammertse, P.: Hapticmaster – a generic force controlled robot for human interaction. Industrial Robot: An International Journal 30(6), 515–524 (2003)CrossRefGoogle Scholar
  26. 26.
    Willow Garage. Robot Operating System, ROS (2009),

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  • Wilm Decré
    • 1
    Email author
  • Herman Bruyninckx
    • 1
  • Joris De Schutter
    • 1
  1. 1.Department of Mechanical Engineering, Division PMAKatholieke Universiteit LeuvenLeuvenBelgium

Personalised recommendations