Kinesthetic Teaching Using Assisted Gravity Compensation for Model-Free Trajectory Generation in Confined Spaces

  • Jochen J. SteilEmail author
  • Christian Emmerich
  • Agnes Swadzba
  • Ricarda Grünberg
  • Arne Nordmann
  • Sebastian Wrede
Conference paper
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 94)


The presented work approaches programming of redundant robots such as the KUKA Lightweight Robot IV in a co-worker scenario from a user-centered point of view. It specifically asks, how the user’s implicit knowledge about the scene and the task can be transferred effectively to the robot through kinesthetic teaching. It proposes a new method to visualize the implicit scene model conveyed by the user when teaching a respective inverse kinematics and measures generalization by the robot. Based on these insights and empirical results from a previously performed user study, the present study argues that physical guidance of a task in confined spaces with static obstacles is too difficult to achieve in a single interaction. Summarizing earlier results and putting them into context, it is shown how to assist users to remedy this issue. The key is to divide the process in an explicit configuration phase for teaching the implicit scene model and a subsequent already assisted programming phase to teach the task based on a particular assisted gravity compensation mode. Further results from the user study confirm that this renders kinesthetic teaching in confined spaces feasible and enables a flexible and fast reconfiguration of the robot.


kinesthetic teaching redundant robots implicit scene modeling assisted gravity compensation user study physical human-robot interaction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    European Strategic Robotics Platform. Robotic visions to 2020 and beyond – The strategic research agenda (SRA) for robotics in Europe. Technical report, European Robotics Technology Platform, Brussels (2009)Google Scholar
  2. 2.
    Haddadin, S., Suppa, M., Fuchs, S., Bodenmüller, T., Albu-Schäffer, A., Hirzinger, G.: Towards the robotic co-worker. In: Pradalier, C., Siegwart, R., Hirzinger, G. (eds.) Robotics Research. STAR, vol. 70, pp. 261–282. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Groothuis, S.S., Stramigioli, S., Carloni, R.: Lending a helping hand: Toward novel assistive robotic arms. IEEE Robotics & Automation Magazine 20(1), 20–29 (2013)CrossRefGoogle Scholar
  4. 4.
    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 34(5), 376–385 (2007)CrossRefGoogle Scholar
  5. 5.
    Tsagarakis, N., Sardellitti, I., Caldwell, D.: A new variable stiffness actuator (CompAct-VSA): Design and modelling. In: Proc. IROS, pp. 378–383 (2011)Google Scholar
  6. 6.
    Bischoff, R., Kurth, J., Schreiber, G., Koeppe, R., Albu-Schäffer, A., Beyer, A., Eiberger, O., Haddadin, S., Stemmer, A., Grunwald, G., Hirzinger, G.: The KUKA-DLR Lightweight Robot arm a new reference platform for robotics research and manufacturing. In: Joint 41st International Symposium on Robotics and 6th German Conference on Robotics, pp. 741–748 (2010)Google Scholar
  7. 7.
    Grzesiak, A., Becker, R., Verl, A.: The bionic handling assistant: A success story of additive manufacturing. Assembly Automation 31(4), 329–333 (2011)CrossRefGoogle Scholar
  8. 8.
    Conkur, E.S., Buckingham, R.: Clarifying the definition of redundancy as used in robotics. Robotica 15(5), 583–586 (1997)CrossRefGoogle Scholar
  9. 9.
    Daimler, A.G.: Leichtbauroboter im Piloteinsatz im Mercedes-Benz Werk Untertürkheim (Lightweight robots employed in pilot application at Mercedes-Benz site Untertürkheim), Press release. Daimler AG (2009),
  10. 10.
    Akgun, B., Cakmak, M., Yoo, J.W., Thomaz, A.L.: Trajectories and keyframes for kinesthetic teaching: A human-robot interaction perspective. In: Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 391–398 (2012)Google Scholar
  11. 11.
    Breazeal, C., Siegel, M., Berlin, M., Gray, J., Grupen, R., Deegan, P., Weber, J., Narendran, K., McBean, J.: Mobile, dexterous, social robots for mobile manipulation and human-robot interaction. In: Proc. ACM SIGGRAPH (2008)Google Scholar
  12. 12.
    Schaal, S., Ijspeert, A., Billard, A.: Computational approaches to motor learning by imitation. Philosophical Transactions of the Royal Society of London Series B Biological Sciences 358(1431), 537–547 (2003)CrossRefGoogle Scholar
  13. 13.
    Billard, A., Calinon, S., Dillmann, R., Schaal, S.: Robot Programming by Demonstration. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, ch. 59, pp. 1371–1394. Springer, Heidelberg (2007)Google Scholar
  14. 14.
    Calinon, S., Billard, A.: Incremental learning of gestures by imitation in a humanoid robot. In: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 255–262. ACM (2007)Google Scholar
  15. 15.
    Mühlig, M., Gienger, M., Hellbach, S., Steil, J.J., Goerick, C.: Task-level imitation learning using variance-based movement optimization. In: IEEE Conf. Robotics and Automation, pp. 1177–1184. IEEE (2009)Google Scholar
  16. 16.
    Mühlig, M., Gienger, M., Steil, J.J.: Interactive imitation learning of object movement skills. Autonomous Robots 32, 97–114 (2012)CrossRefGoogle Scholar
  17. 17.
    Vijayakumar, S., D’souza, A., Shibata, T., Conradt, J., Schaal, S.: Statistical learning for humanoid robots. Autonomous Robots 12, 55–69 (2002)CrossRefzbMATHGoogle Scholar
  18. 18.
    Wrede, S., Emmerich, C., Grünberg, R., Nordmann, A., Swadzba, A., Steil, J.J.: A User Study on Kinesthetic Teaching and Learning for Efficient Reconfiguration of Redundant Robots. Journal of Human-Robot Interaction 2, 56–81 (2013)CrossRefGoogle Scholar
  19. 19.
    Khansari-Zadeh, S., Billard, A.: A dynamical system approach to realtime obstacle avoidance. Autonomous Robots, 1–22 (2012)Google Scholar
  20. 20.
    Kormushev, P., Calinon, S., Caldwell, D.G.: Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input. Advanced Robotics 25(5), 581–603 (2011)CrossRefGoogle Scholar
  21. 21.
    Emmerich, C., Nordmann, A., Swadzba, A., Steil, J.J., Wrede, S.: Assisted gravity compensation to cope with the complexity of kinesthetic teaching on redundant robots. In: International Conference on Robotics and Automation, Karlsruhe, pp. 4307–4313 (2013)Google Scholar
  22. 22.
    Albu-Schäffer, A., Ott, C., Hirzinger, G.: A Unified Passivity Based Control Framework for Position, Torque and Impedance Control of Flexible Joint Robots. In: Thrun, S., Brooks, R., Durrant-Whyte, H. (eds.) Robotics Research. STAR, vol. 28, pp. 5–21. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  23. 23.
    Neumann, K., Rolf, M., Steil, J.J.: Learning inverse kinematics for pose-constraint bi-manual movements. In: Proceedings of the 11th International Conference on Simulation of Adaptive Behavior: from Animals to Animats, pp. 478–488 (2010)Google Scholar
  24. 24.
    Nordmann, A., Emmerich, C., Rüther, S., Lemme, A., Wrede, S., Steil, J.J.: Teaching Nullspace Constraints in Physical Human-Robot Interaction using Reservoir Computing. In: IEEE Int. Conf. on Robotics and Automation, pp. 1868–1875 (2012)Google Scholar
  25. 25.
    Phung, A.S., Malzahn, J., Hoffmann, F., Bertram, T.: Data based kinematic model of a multi-flexible-link robot arm for varying payloads. In: IEEE. Conf. Robotics and Biomimetics, pp. 1255–1260 (2011)Google Scholar
  26. 26.
    Rolf, M., Steil, J.J., Gienger, M.: Efficient exploration and learning of whole body kinematics. In: IEEE 8th International Conference on Development and Learning (ICDL 2009), Shanghai, CH, pp. 1–7 (2009)Google Scholar
  27. 27.
    Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics 2(2), 107–122 (2011)CrossRefGoogle Scholar
  28. 28.
    Neumann, K., Emmerich, C., Steil, J.J.: Regularization by intrinsic plasticity and its synergies with recurrence for random projection methods. Journal of Intelligent Learning Systems and Applications 4(3), 230–246 (2012)CrossRefGoogle Scholar
  29. 29.
    Grupen, R.A., Huber, M.: A framework for the development of robot behavior. In: AAAI Spring Symposium Series: Developmental Robotics. Stanford University (2005)Google Scholar
  30. 30.
    Kaber, D.B., Riley, J.M.: Effects of visual interface design, and control mode and latency on performance, telepresence and workload in a teleoperation task. In: Proc. XIVth Triennial Congress of the International Ergonomics Association and 44th Annual Meeting of the Human Factors and Ergonomics Society, pp. 503–506 (2000)Google Scholar
  31. 31.
    Steinfeld, A., Fong, T., Kaber, D.: Common metrics for human-robot interaction. In: HRI 2006: Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction, pp. 33–40 (2006)Google Scholar
  32. 32.
    Martin, D.P.: Resolution of kinematic redundancy using optimization techniques. IEEE Transactions on Robotics and Automation 5(4), 529–533 (1989)CrossRefGoogle Scholar
  33. 33.
    Iossifidis, I., Steinhage, A.: Controlling a redundant robot arm by means of a haptic sensor. In: VDI BERICHTE: ROBOTIK 2002, Leistungsstand - Anwendungen - Visionen, pp. 269–274 (2002)Google Scholar
  34. 34.
    Kendall, D.G.: A survey of the statistical of shape theory. Statistical Science 4(2), 87–99 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Nordmann, A., Rolf, M., Wrede, S.: Software abstractions for simulation and control of a continuum robot. In: Noda, I., Ando, N., Brugali, D., Kuffner, J.J. (eds.) SIMPAR 2012. LNCS, vol. 7628, pp. 113–124. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  36. 36.
    Schreiber, G., Stemmer, A., Bischoff, R.: The fast research interface for the KUKA Lightweight Robot. In: IEEE ICRA 2010 Workshop on Innovative Robot Control Architectures, pp. 15–21 (2010)Google Scholar
  37. 37.
    Alami, R., Albu-Schäffer, A., Bicchi, A., Bischoff, R., Chatila, R., Hirzinger, G.: Safe and dependable pHRI in anthropic domains: State of the art and challenges. In: Proceedings of the 4th IARP/IEEE-RAS/EURON Workshop on Technical Challenges for Dependable Robots in Human Environments, vol. (1) (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jochen J. Steil
    • 1
    Email author
  • Christian Emmerich
    • 1
  • Agnes Swadzba
    • 1
  • Ricarda Grünberg
    • 1
    • 2
  • Arne Nordmann
    • 1
  • Sebastian Wrede
    • 1
  1. 1.Institute for Cognition and Robotics (CoR-Lab) and Faculty of TechnologyBielefeld UniversityBielefeldGermany
  2. 2.Research Group on Gender & Emotion, Faculty of PsychologyBielefeld UniversityBielefeldGermany

Personalised recommendations