Advertisement

Autonomous Robots

, Volume 43, Issue 6, pp 1327–1342 | Cite as

A human inspired handover policy using Gaussian Mixture Models and haptic cues

  • Antonis Sidiropoulos
  • Efi Psomopoulou
  • Zoe DoulgeriEmail author
Article
Part of the following topical collections:
  1. Special Issue: Learning for Human-Robot Collaboration

Abstract

A handover strategy is proposed that aims at natural and fluent robot to human object handovers. For the approaching phase, a globally asymptotically stable dynamical system (DS) is utilized, trained from human demonstrations and exploiting the existence of mirroring in the human wrist motion. The DS operates in the robot task space thus achieving independence with respect to the robot platform, encapsulating the position and orientation of the human wrist within a single DS. It is proven that the motion generated by such a DS, having as target the current wrist pose of the receiver’s hand, is bounded and converges to the previously unknown handover location. Haptic cues based on load estimates at the robot giver ensure full object load transfer before grip release. The proposed strategy is validated with simulations and experiments in real settings.

Keywords

Programming by Demonstration Gaussian Mixture Model Physical human-robot interaction Haptic communication 

Supplementary material

10514_2018_9705_MOESM1_ESM.mp4 (77.2 mb)
Supplementary material 1 (mp4 79014 KB)

References

  1. Amin, T.B., & Mahmood, I. (2008). Speech recognition using dynamic time warping. In 2nd International conference on advances in space technologies (pp. 74–79).Google Scholar
  2. Arimoto, S. (2008). Control theory of multi-fingered hands: A modelling and analytical-mechanics approach for dexterity and intelligence. London: Springer.Google Scholar
  3. Ben Amor, H., Neumann, G., Kamthe, S., Kroemer, O., & Peters, J. (2014). Interaction primitives for human-robot cooperation tasks. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 2831–2837). IEEE (Online). Available http://ieeexplore.ieee.org/document/6907265/.
  4. Bischoff, R., & Guhl, T. (2010). The strategic research agenda for robotics in Europe [industrial activities]. IEEE Robotics Automation Magazine, 17(1), 15–16.CrossRefGoogle Scholar
  5. Cakmak, M., Srinivasa, S. S., Lee, M. K., Forlizzi, J., & Kiesler, S. (2011). Human preferences for robot-human hand-over configurations. In IEEE/RSJ International Conference on Intelligent Robots and Systems (pp 1986–1993).Google Scholar
  6. Calinon, S., D’halluin, F., Sauser, E. L., Caldwell, D. G., & Billard, A. G. (2010). Learning and reproduction of gestures by imitation. IEEE Robotics Automation Magazine, 17(2), 44–54.CrossRefGoogle Scholar
  7. Chan, W. P., Parker, C. A., Van der Loos, H. M., & Croft, E. A. (2012). Grip forces and load forces in handovers. In Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction: HRI ’12 (pp. 9–16).Google Scholar
  8. Chan, W. P., Parker, C. A., Van der Loos, H. M., & Croft, E. a. (2013). A human-inspired object handover controller. The International Journal of Robotics Research, 32(8), 971–983.CrossRefGoogle Scholar
  9. Fulling, S. A., Sinyakov, M. N., & Tishchenko, S. V. (2011). Linearity and the mathematics of several variables. Singapore: World Scientific.Google Scholar
  10. Gribovskaya, E., & Billard, A. (2009). Learning nonlinear multi-variate motion dynamics for real-time position and orientation control of robotic manipulators. In 9th IEEE-RAS International Conference on Humanoid Robots (pp. 472–477).Google Scholar
  11. Gribovskaya, E., Khansari-Zadeh, S., & Billard, A. (2011). Learning non-linear multivariate dynamics of motion in robotic manipulators. The International Journal of Robotics Research, 30(1), 80–117.CrossRefGoogle Scholar
  12. 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
  13. Huber, M., Rickert, M., Knoll, A., Brandt, T., & Glasauer, S. (2008). Human–robot interaction in handing-over tasks. In RO-MAN 2008: The 17th IEEE International Symposium on Robot and Human Interactive Communication (pp. 107–112). IEEE.Google Scholar
  14. Ijspeert, A. J., Nakanishi, J., & Schaal, S. (2002). Movement imitation with nonlinear dynamical systems in humanoid robots. Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2, 1398–1403.CrossRefGoogle Scholar
  15. Khansari Zadeh, S. M., & Billard, A. (2009). Learning and control of uav maneuvers based on demonstrations. Presented at Robotics Science and Systems, Seattle, June 28 July 1, 2009.Google Scholar
  16. Khansari-Zadeh, S. M., & Billard, A. (2010). Imitation learning of globally stable non-linear point-to-point robot motions using nonlinear programming. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 2676–2683).Google Scholar
  17. Kim, I., Nakazawa, N., & Inooka, H. (2002). Control of a robot hand emulating human’s hand-over motion. Mechatronics, 12(1), 55–69.CrossRefGoogle Scholar
  18. Kupcsik, A., Hsu, D., & Lee, W. S. (2015). Learning dynamic robot-to-human object handover from human feedback. In International Symposium on Robotics Research (pp. 1–11) (Online). Available http://arxiv.org/abs/1603.06390.
  19. Lang, M., Kleinsteuber, M., Dunkley, O., & Hirche, S. (2015). Gaussian process dynamical models over dual quaternions. In European Control Conference (ECC) (pp. 2847–2852).Google Scholar
  20. Mason, A. H., & MacKenzie, C. L. (2005). Grip forces when passing an object to a partner. Experimental Brain Research, 163(2), 173–187.CrossRefGoogle Scholar
  21. Medina, J. R., Duvallet, F., Karnam, M., & Billard, A. (2016). A human-inspired controller for fluid human-robot handovers. In Conference: 2016 IEEE-RAS International Conference on Humanoid Robots.Google Scholar
  22. Murray, R., & Sastry, S. (1994). A mathematical introduction to robotic manipulation. Boca Raton: CRC Press.zbMATHGoogle Scholar
  23. Pastor, P., Hoffmann, H., Asfour, T., & Schaal, S. (2009). Learning and generalization of motor skills by learning from demonstration. In IEEE International Conference on Robotics and Automation (pp. 763–768).Google Scholar
  24. Pastor, P., Kalakrishnan, M., Meier, F., Stulp, F., Buchli, J. Theodorou, E., & Schaal, S. (2012). From dynamic movement primitives to associative skill memories. Robotics and Autonomous Systems.Google Scholar
  25. Pastor, P., Righetti, L., Kalakrishnan, M., & Schaal, S. (2011). Online movement adaptation based on previous sensor experiences. In IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 365–371).Google Scholar
  26. Prada, M., & Remazeilles, A. (2012). Dynamic movement primitives for human robot interaction. In IEEE/RSJ IROS, workshop on robot motion planning: online, reactive and in Real-time, Algarve, Portugal.Google Scholar
  27. Prada, M., Remazeilles, A., Koene, A., & Endo, S. (2013). Dynamic movement primitives for human-robot interaction: Comparison with human behavioral observation. In IEEE/RSJ international conference on intelligent robots and systems (pp. 1168–1175).Google Scholar
  28. Prada, M., Remazeilles, A., Koene, A., & Endo, S. (2014). Implementation and experimental validation of dynamic movement primitives for object handover. In IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 2146–2153).Google Scholar
  29. Psomopoulou, E., & Doulgeri, Z. (2014). A robot hand-over control scheme for human-like haptic interaction. In 22nd Mediterranean conference of control and automation (MED) (pp. 1470–1475).Google Scholar
  30. Psomopoulou, E., & Doulgeri, Z. (2015). A human inspired stable object load transfer for robots in hand-over tasks. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 491–496).Google Scholar
  31. Shukla, A., & Billard, A. (2012). Coupled dynamical system based armhand grasping model for learning fast adaptation strategies. Robotics and Autonomous Systems, 60(3), 424 – 440. Autonomous Grasping (Online). Available http://www.sciencedirect.com/science/article/pii/S0921889011001576.
  32. Siciliano, B., Sciavico, L., Villani, L., & Oriolo, J. (2010). Robotics: modeling, planning and control. London: Springer-Verlag Limited.Google Scholar
  33. Silverio, J., Rozo, L., Calinon, S., & Caldwell, D. G. (2015). Learning bimanual end-effector poses from demonstrations using task-parameterized dynamical systems. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Vol. 2015, pp. 464–470). IEEE.Google Scholar
  34. Sisbot, E. A., Marin-Urias, L. F., Broquère, X., Sidobre, D., & Alami, R. (2010). Synthesizing robot motions adapted to human presence. International Journal of Social Robotics, 2(3), 329–343.  https://doi.org/10.1007/s12369-010-0059-6.
  35. Strabala, K., Lee, M. K., Dragan, A., Forlizzi, J., Srinivasa, S., Cakmak, M., et al. (2013). Towards seamless human-robot handovers. Journal of Human-Robot Interaction, 1(1), 1–23.Google Scholar
  36. Ude, A., Nemec, B., Petri, T., & Morimoto, J. (2014). Orientation in Cartesian space dynamic movement primitives. In IEEE International conference on robotics and automation (ICRA) (pp. 2997–3004).Google Scholar
  37. Waldhart, J., Gharbi, M., & Alami, R. (2015). Planning handovers involving humans and robots in constrained environment. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 6473–6478).Google Scholar
  38. Wang, R., Wu, Y., Chan, W. L., & Tee, K. P. (2016). Dynamic movement primitives plus: For enhanced reproduction quality and efficient trajectory modification using truncated kernels and local biases. In IEEE/RSJ International conference on intelligent robots and systems (IROS) (pp. 3765–3771).Google Scholar
  39. Widmann, D. (2016). An adaptive control approach based on dynamic movement primitives for human-robot handover. Masters thesis in Systems, Control and Mechatronics, Chalmers University of Technology, Department of Signals and Systems, Gothenburg, Sweden.Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Center for Research and Technology Hellas (CERTH)ThessalonikiGreece
  2. 2.Department of Electrical and Computer EngineeringAristotle University of ThessalonikiThessalonikiGreece

Personalised recommendations