Autonomous Robots

, Volume 36, Issue 1–2, pp 123–136 | Cite as

Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach

  • Luka PeternelEmail author
  • Tadej Petrič
  • Erhan Oztop
  • Jan Babič


We propose an approach to efficiently teach robots how to perform dynamic manipulation tasks in cooperation with a human partner. The approach utilises human sensorimotor learning ability where the human tutor controls the robot through a multi-modal interface to make it perform the desired task. During the tutoring, the robot simultaneously learns the action policy of the tutor and through time gains full autonomy. We demonstrate our approach by an experiment where we taught a robot how to perform a wood sawing task with a human partner using a two-person cross-cut saw. The challenge of this experiment is that it requires precise coordination of the robot’s motion and compliance according to the partner’s actions. To transfer the sawing skill from the tutor to the robot we used Locally Weighted Regression for trajectory generalisation, and adaptive oscillators for adaptation of the robot to the partner’s motion.


Human–robot interaction Sensorimotor learning Teleoperation Impedance control Adaptive oscillator 



We thank L. Žlajpah for discussions and for providing us with the robot control infrastructure. This work was supported by the Slovenian Research Agency, programme P2-0076 and grant J2-2272, and the Slovenian Ministry of Higher Education, Science and Technology.

Supplementary material

Supplementary material 1 (mpg 25622 KB)


  1. Adorno, B. V., Bo, A. P. L., Fraisse, P., & Poignet, P. (2011). Towards a cooperative framework for interactive manipulation involving a human and a humanoid. In 2011 IEEE international conference on robotics and automation (ICRA) (pp. 3777–3783).Google Scholar
  2. Ajoudani, A., Tsagarakis, N. G., & Bicchi, A. (2012). Tele-impedance: Teleoperation with impedance regulation using a body-machine interface. The International Journal of Robotics Research, 31(13), 1642–1656.CrossRefGoogle Scholar
  3. Argall, B. D., & Billard, A. (2010). A survey of tactile human–robot interactions. Robotics and Autonomous Systems, 58(10), 1159–1176.CrossRefGoogle Scholar
  4. Argall, B. D., Chernova, S., Veloso, M., & Browning, B. (2009). A survey of robot learning from demonstration. Robotics and Autonomous Systems, 57(5), 469–483.CrossRefGoogle Scholar
  5. Atkeson, C. G., Hale, J. G., Pollick, F., Riley, M., Kotosaka, S., Schaal, S., et al. (2000). Using humanoid robots to study human behavior. IEEE Intelligent Systems, 15(4), 46–56.CrossRefGoogle Scholar
  6. Babič, J., Hale, J. G., & Oztop, E. (2011). Human sensorimotor learning for humanoid robot skill synthesis. Adaptive Behavior—Animals, Animats, Software Agents, Robots, Adaptive Systems, 19, 250–263.Google Scholar
  7. Ben Amor, H., Berger, E., Vogt, D., & Jung, B. (2009). Kinesthetic bootstrapping: Teaching motor skills to humanoid robots through physical interaction. In Proceedings of the 32nd annual German conference on advances in artificial intelligence, KI’09 (pp. 492–499). Berlin, Heidelberg: Springer.Google Scholar
  8. Ben Amor, H., Saxena, A., Kroemer, O., & Peters, J. (2012). Workshop: Beyond robot grasping—Modern approaches for learning dynamic manipulation. In 2012 IEEE/RSJ international conference on intelligent robots and systems.Google Scholar
  9. Billard, A., Calinon, S., Dillmann, R., & Schaal, S. (2008). Robot programming by demonstration. In B. Siciliano & O. Khatib (Eds.), Springer handbook of robotics (pp. 1371–1394). Berlin: Springer.CrossRefGoogle Scholar
  10. Burdet, E., Osu, R., Franklin, D. W., Milner, T. E., & Kawato, M. (2001). The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature, 414(6862), 446–449.CrossRefGoogle Scholar
  11. Chiaverini, S. (1997). Singularity-robust task-priority redundancy resolution for real-time kinematic control of robot manipulators. IEEE Transactions on Robotics and Automation, 13(3), 399–410.Google Scholar
  12. De Santis, A., Siciliano, B., De Luca, A., & Bicchi, A. (2008). An atlas of physical human–robot interaction. Mechanism and Machine Theory, 43(3), 253–270.CrossRefzbMATHGoogle Scholar
  13. Edsinger, A., & Kemp, C. C. (2007). Human–robot interaction for cooperative manipulation: Handing objects to one another. In The 16th IEEE international symposium on robot and human interactive communication (2007) (pp. 1167–1172).Google Scholar
  14. Evrard, P., Gribovskaya, E., Calinon, S., Billard, A., & Kheddar, A. (2009). Teaching physical collaborative tasks: object-lifting case study with a humanoid. In IEEE-RAS international conference on humanoid robots (pp. 399–404).Google Scholar
  15. Franklin, D. W., Burdet, E., Osu, R., Kawato, M., & Milner, T. (2003a). Functional significance of stiffness in adaptation of multijoint arm movements to stable and unstable dynamics. Experimental Brain Research, 151(2), 145–157.CrossRefGoogle Scholar
  16. Franklin, D. W., Osu, R., Burdet, E., Kawato, M., & Milner, T. E. (2003b). Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model. Journal of Neurophysiology, 90, 3270–3282.Google Scholar
  17. Gams, A., Ijspeert, A. J., Schaal, S., & Lenarčič, J. (2009). On-line learning and modulation of periodic movements with nonlinear dynamical systems. Autonomous Robots, 27(1), 3–23.CrossRefGoogle Scholar
  18. Gams, A., Petric, T., Ude, A., & Zlajpah, L. (2012). Performing periodic tasks: On-line learning, adaptation and synchronization with external signals. In R. Zaier (Ed.), The future of humanoid robots—research and applications (pp. 1–28). InTech.Google Scholar
  19. Haykin, S. (1999). Neural networks: A comprehensive foundation. New Jersey: Prentice Hall.zbMATHGoogle Scholar
  20. Hersch, M., Guenter, F., Calinon, S., & Billard, A. (2008). Dynamical system modulation for robot learning via kinesthetic demonstrations. IEEE Transactions on Robotics, 24(6), 1463–1467.CrossRefGoogle Scholar
  21. Ijspeert, A. J., Nakanishi, J., & Schaal, S. (2002). Movement imitation with nonlinear dynamical systems in humanoid robots. In Proceedings 2002 IEEE international conference on robotics and automation (vol. 2, pp. 1398–1403).Google Scholar
  22. Ikemoto, S., Ben Amor, H., Minato, T., Jung, B., & Ishiguro, H (2012). Physical human–robot interaction: Mutual learning and adaptation. IEEE Robotics Automation Magazine, 19(4), 24–35.Google Scholar
  23. Kober, J., Wilhelm, A., Oztop, E., & Peters, J. (2012). Reinforcement learning to adjust parametrized motor primitives to new situations. Autonomous Robots, 33(4), 361–379.CrossRefGoogle Scholar
  24. Kormushev, P., Calinon, S., & Caldwell, D. G. (2011). Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input. Advanced Robotics, 25(5), 581–603.CrossRefGoogle Scholar
  25. Kremer, P., Wimböck, T., Artigas, J., Schätzle, S., Jöhl, K., Schmidt, F., et al. (2009). Multimodal telepresent control of dlr’s rollin’ justin. In Proceedings of the 2009 IEEE international conference on robotics and automation, ICRA’09 (pp. 3096–3097). Piscataway, NJ: IEEE Press.Google Scholar
  26. Kroemer, O. B., Detry, R., Piater, J., & Peters, J. (2010). Combining active learning and reactive control for robot grasping. Robotics and Autonomous Systems, 58(9), 1105–1116.CrossRefGoogle Scholar
  27. Kushida, D., Nakamura, M., Goto, S., & Kyura, N. (2001). Human direct teaching of industrial articulated robot arms based on force-free control. Artificial Life and Robotics, 5(1), 26–32.CrossRefGoogle Scholar
  28. Lallee, S., Yoshida, E., Mallet, A., Nori, F., Natale, L., Metta, G., et al. (2010). Human–robot cooperation based on interaction learning. In O. Sigaud & J. Peters (Eds.), From motor learning to interaction learning in robots volume 264 of studies in computational intelligence (pp. 491–536). Berlin: Springer.Google Scholar
  29. Maciejewski, A. A., & Klein, C. A. (1985). Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments. The International Journal of Robotics Research, 4(3), 109–117.CrossRefGoogle Scholar
  30. Moore, B., & Oztop, E. (2012). Robotic grasping and manipulation through human visuomotor learning. Robotics and Autonomous Systems, 60(3), 441–451.CrossRefGoogle Scholar
  31. Muelling, K., Kober, J., Kroemer, O., & Peters, J. (2012). Learning to select and generalize striking movements in robot table tennis. In AAAI fall symposium series.Google Scholar
  32. Niemeyer, G., Preusche, C., & Hirzinger, G. (2008). Telerobotics. In B. Siciliano & O. Khatib (Eds.), Springer handbook of robotics (pp. 741–757). Berlin: Springer.CrossRefGoogle Scholar
  33. Osu, R., & Gomi, H. (1999). Multijoint muscle regulation mechanism examined by measured human arm stiffness and emg signals. Journal of Neurophysiology, 81, 1458–1468.Google Scholar
  34. Oztop, E., Lin, L.-H., Kawato, M., & Cheng, G. (2006). Dexterous skills transfer by extending human body schema to a robotic hand. In 2006 6th IEEE-RAS international conference on humanoid robots (pp. 82–87).Google Scholar
  35. Peternel, L., & Babič, J. (2013a). Humanoid robot posture-control learning in real-time based on human sensorimotor learning ability. In 2013 IEEE international conference on robotics and automation (pp. 5309–5314).Google Scholar
  36. Peternel, L., & Babič, J. (2013b). Learning of compliant human–robot interaction using full-body haptic interface. Advanced Robotics, 27(13), 1003–1012.Google Scholar
  37. Petrič, T., Gams, A., Ijspeert, A. J., & Žlajpah, L. (2011). On-line frequency adaptation and movement imitation for rhythmic robotic tasks. The International Journal of Robotics Research, 30(14), 1775–1788.CrossRefGoogle Scholar
  38. Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. International Journal of Neural Systems, 14(2), 69–106.Google Scholar
  39. Sauser, E. L., Argall, B. D., Metta, G., & Billard, A. G. (2012). Iterative learning of grasp adaptation through human corrections. Robotics and Autonomous Systems, 60(1), 55–71.CrossRefGoogle Scholar
  40. Schaal, S. (1999). Is imitation learning the route to humanoid robots? Trends in Cognitive Sciences, 3(6), 233–242.CrossRefGoogle Scholar
  41. Schaal, S., & Atkeson, C. G. (1998). Constructive incremental learning from only local information. Neural Computation, 10(8), 2047–2084.CrossRefGoogle Scholar
  42. Selen, L. P. J., Beek, P. J., & van Dieëbn, J. H. (2005). Can co-activation reduce kinematic variability? A simulation study. Biological Cybernetics, 93(5), 373–381.CrossRefzbMATHGoogle Scholar
  43. Tsumugiwa, T., Yokogawa, R., & Hara, K. (2002). Variable impedance control with virtual stiffness for human-robot cooperative peg-in-hole task. In 2002 IEEE/RSJ international conference on intelligent robots and systems (vol. 2, pp. 1075–1081).Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Luka Peternel
    • 1
    Email author
  • Tadej Petrič
    • 1
  • Erhan Oztop
    • 2
  • Jan Babič
    • 1
  1. 1.Department of Automation, Biocybernetics and RoboticsJožef Stefan Institute, Jamova cesta 39LjubljanaSlovenia
  2. 2.Computer ScienceÖzyeğin UniversityAlemdagTurkey

Personalised recommendations