Autonomous Robots

, Volume 42, Issue 5, pp 1053–1065 | Cite as

One-shot learning of human–robot handovers with triadic interaction meshes

  • David Vogt
  • Simon Stepputtis
  • Bernhard Jung
  • Heni Ben Amor
Part of the following topical collections:
  1. Special Issue: Learning for Human-Robot Collaboration


We propose an imitation learning methodology that allows robots to seamlessly retrieve and pass objects to and from human users. Instead of hand-coding interaction parameters, we extract relevant information such as joint correlations and spatial relationships from a single task demonstration of two humans. At the center of our approach is an interaction model that enables a robot to generalize an observed demonstration spatially and temporally to new situations. To this end, we propose a data-driven method for generating interaction meshes that link both interaction partners to the manipulated object. The feasibility of the approach is evaluated in a within user study which shows that human–human task demonstration can lead to more natural and intuitive interactions with the robot.


Human–human demonstration Human–robot interaction Handover Interaction mesh 


  1. Admoni, H., Dragan, A., Srinivasa, S, & Scassellati, B. (2014). Deliberate delays during robot-to-human handovers improve compliance with gaze communication. In International conference on human-robot interaction (HRI).Google Scholar
  2. Bankó, Z., & Abonyi, J. (2012). Correlation based dynamic time warping of multivariate time series. Expert Systems with Applications, 39(17), 12814–12823.CrossRefGoogle Scholar
  3. Ben Amor, H. (2010). Imitation learning of motor skills for synthetic humanoids. Ph.D. dissertation, Technische Universität Bergakademie Freiberg.Google Scholar
  4. Ben Amor, H., Neumann, G., Kamthe, S., Kroemer, O., & Peters, J. (2014). Interaction primitives for human-robot cooperation tasks. In 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, May 2014 (pp. 2831–2837).Google Scholar
  5. Berndt, D. J., & Clifford, J. (1994). Using dynamic time warping to find patterns in time series. In Knowledge Discovery in Databases (pp. 359–370). Washington: Seattle.Google Scholar
  6. Dehais, F., Sisbot, E. A., Alami, R., & Causse, M. (2011). Physiological and subjective evaluation of a humanrobot object hand-over task. Applied Ergonomics, 42(6), 785–791.CrossRefGoogle Scholar
  7. Dragan, A., Lee, K., & Srinivasa, S. (2013). Legibility and predictability of robot motion. Human-Robot Interaction.Google Scholar
  8. Duvallet, F., Karnam, M., & Billard, A. (2016) . A human-inspired controller for fluid human-robot handovers. In Humanoids 2016—16th IEEE-RAS international conference on humanoid robots (pp. 324–331).Google Scholar
  9. Ewerton, M., Neumann, G., Lioutikov, R., Amor, H. B. , Peters, J., & Maeda, G. (2015). Learning multiple collaborative tasks with a mixture of interaction primitives. In IEEE International conference on robotics and automation (pp. 1535–1542).Google Scholar
  10. Ho, E. S. L., Chan, J. C. P., Komura, T., & Leung, H. (2013). Interactive partner control in close interactions for real-time applications. ACM Transactions on Multimedia Computing, Communications, and Applications, 9(3), 1–19.CrossRefGoogle Scholar
  11. Ho, E. S. L., Komura, T., & Tai, C.-L. (2010). Spatial relationship preserving character motion adaptation. ACM Transactions on Graphics, 29(4), 1.CrossRefGoogle Scholar
  12. Huang, C.-M., Cakmak, M., & Mutlu, B. (2015). Adaptive coordination strategies for human-robot handovers. In Robotics: Science and Systems XI. Robotics: Science and Systems Foundation, July.Google Scholar
  13. Ivan, V., Zarubin, D., Toussaint, M., Komura, T., & Vijayakumar, S. (2013). Topology-based representations for motion planning and generalization in dynamic environments with interactions. The International Journal of Robotics Research, 32(9–10), 1151–1163.CrossRefGoogle Scholar
  14. Kupcsik, A., Hsu, D., & Lee, W. S. (2016). Learning dynamic robot-to-human object handover from human feedback. CoRR, (Vol. abs/1603.06390, 2016) (Online). Available:
  15. Lee, D., Ott, C., & Nakamura, Y. (2010). Mimetic Communication Model with Compliant Physical Contact in Human-Humanoid Interaction. The International Journal of Robotics Research, 29(13), 1684–1704.CrossRefGoogle Scholar
  16. Mainprice, J., Gharbi, M., Siméon, T., & Alami, R. (2012). Sharing effort in planning human-robot handover tasks. In IEEE Ro-man: The 21st IEEE International Symposium on Robot and Human Interactive Communication. IEEE, 2012 (pp. 764–770).Google Scholar
  17. Quispe, A. H., Ben Amor, H., & Stilman, M. (2014). Handover planning for every occasion. In 2014 IEEE-RAS International Conference on Humanoid Robots. IEEE, November 2014 (pp. 431–436). (Online) Available:
  18. Schaal, S. (1999). Is Imitation Learning the Route to Humanoid Robots? Trends in Cognitive Sciences, 3(6), 233–242.CrossRefGoogle Scholar
  19. Sorkine, O., Cohen-Or, D., Lipman, Y., Alexa, M., Rössl, C., & Seidel, H.-P. (2004) . Laplacian surface editing. In Proceedings of the EUROGRAPHICS/ACM SIGGRAPH Symposium on Geometry Processing. ACM Press (pp. 179–188).Google Scholar
  20. Strabala, K., Lee, M. K., Dragan, A., Forlizzi, J., Srinivasa, S. S., Cakmak, M., et al. (2013). Toward Seamless Human-robot handovers. Journal of Human-Robot Interaction, 2(1), 112–132.Google Scholar
  21. Vogt, D., Lorenz, B., Grehl, S., & Jung, B. (2015). Behavior generation for interactive virtual humans using context-dependent interaction meshes and automated constraint extraction. Computer Animation and Virtual Worlds, 26(3–4), 227–235.CrossRefGoogle Scholar
  22. Yang, Y., Ivan, V., & Vijayakumar, S. (2015). Real-time motion adaptation using relative distance space representation. In International Conference on Advanced Robotics (ICAR), No. ICAR. IEEE, July 2015 (pp. 21–27).Google Scholar
  23. Zheng, M., Moon, A., Croft, E. A., & Meng, M. Q. (2015). Impacts of robot head gaze on robot-to-human handovers. International Journal of Social Robotics, 7(5), 783–798.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Mathematics and InformaticsTechnical University Bergakademie FreibergFreibergGermany
  2. 2.School of Computing, Informatics, Decision Systems EngineeringArizona State UniversityTempeUSA

Personalised recommendations