A human inspired handover policy using Gaussian Mixture Models and haptic cues
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.
KeywordsProgramming by Demonstration Gaussian Mixture Model Physical human-robot interaction Haptic communication
- 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
- Arimoto, S. (2008). Control theory of multi-fingered hands: A modelling and analytical-mechanics approach for dexterity and intelligence. London: Springer.Google Scholar
- 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/.
- 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
- 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
- Fulling, S. A., Sinyakov, M. N., & Tishchenko, S. V. (2011). Linearity and the mathematics of several variables. Singapore: World Scientific.Google Scholar
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- Siciliano, B., Sciavico, L., Villani, L., & Oriolo, J. (2010). Robotics: modeling, planning and control. London: Springer-Verlag Limited.Google Scholar
- 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
- 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.
- 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
- 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
- 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
- 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
- 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