Abstract
In this chapter a novel approach to skill acquisition from human demonstration is presented. Usually the morphology of a robot manipulator is very different from the human arm and cannot simply copy a human motion. Instead the robot has to execute its own version of the skill demonstrated by the operator. Once a skill has been acquired by the robot it must also be able to generalize to other similar skills without starting a new learning process. By using a motion planner that operates in an object-related world-frame called hand-state, we show that this representation simplifies a skill reconstruction and preserves the essential parts of the skill.
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References
Argall, B.D., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robot. Auton. Syst. 57(5), 469–483 (2009). doi:10.1016/j.robot.2008.10.024
Calinon, S., Guenter, F., Billard, A.: On learning, representing, and generalizing a task in a humanoid robot. IEEE Trans. Syst. Man Cybern., Part B 37(2), 286–298 (2007). doi:10.1109/TSMCB.2006.886952
Pardowitz, M., Knoop, S., Dillmann, R., Zöllner, R.D.: Incremental learning of tasks from user demonstrations, past experiences, and vocal comments. IEEE Trans. Syst. Man Cybern., Part B 37(2), 322–332 (2007). doi:10.1109/TSMCB.2006.886951
Skoglund, A., Iliev, B., Kadmiry, B., Palm, R.: Programming by demonstration of pick-and-place tasks for industrial manipulators using task primitives. In: IEEE International Symposium on Computational Intelligence in Robotics and Automation, Jacksonville, Florida, 20–23 June 2007, pp. 368–373 (2007). doi:10.1109/CIRA.2007.382863
Takamatsu, J., Ogawara, K., Kimura, H., Ikeuchi, K.: Recognizing assembly tasks through human demonstration. Int. J. Robot. Res. 26(7), 641–659 (2007). doi:10.1177/0278364907080736
Nehaniv, C.L., Dautenhahn, K.: The correspondence problem. In: Dautenhahn, K., Nehaniv, C. (eds.) Imitation in Animals and Artifacts, pp. 41–61. MIT Press, Cambridge (2002)
Ijspeert, A.J., Nakanishi, J., Schaal, S.: Movement imitation with nonlinear dynamical systems in humanoid robots. In: Proceedings of the 2002 IEEE International Conference on Robotics and Automation, pp. 1398–1403 (2002). doi:10.1109/ROBOT.2002.1014739
Hersch, M., Billard, A.G.: Reaching with multi-referential dynamical systems. Auton. Robots 25(1–2), 71–83 (2008). doi:10.1007/s10514-007-9070-7
Iossifidis, I., Schöner, G.: Dynamical systems approach for the autonomous avoidance of obstacles and joint-limits for an redundant robot arm. In: Proceedings of 2006 IEEE International Conference on Robotics and Automation, pp. 580–585 (2006). doi:10.1109/IROS.2006.282468
Oztop, E., Arbib, M.A.: Schema design and implementation of the grasp-related mirror neurons. Biol. Cybern. 87(2), 116–140 (2002). doi:10.1007/s00422-002-0318-1
Ijspeert, A., Nakanishi, J., Schaal, S.: Trajectory formation for imitation with nonlinear dynamical systems. In: IEEE International Conference on Intelligent Robots and Systems (IROS 2001), vol. 2, pp. 752–757 (2001). doi:0.1109/IROS.2001.976259
Ude, A.: Trajectory generation from noisy positions of object features for teaching robot paths. Robot. Auton. Syst. 11(2), 113–127 (1993)
Billard, A., Epars, Y., Calinon, S., Schaal, S., Cheng, G.: Discovering optimal imitation strategies. Robot. Auton. Syst. 47(2–3), 69–77 (2004). doi:10.1016/j.robot.2004.03.002
Aleotti, J., Caselli, S.: Robust trajectory learning and approximation for robot programming. Robot. Auton. Syst. 54(5), 409–413 (2006). doi:10.1016/j.robot.2006.01.003
Palm, R., Iliev, B., Kadmiry, B.: Recognition of human grasps by time-clustering and fuzzy modeling. Robot. Auton. Syst. 57(5), 484–495 (2009). doi:10.1016/j.robot.2008.10.012
Palm, R., Iliev, B.: Learning of grasp behaviors for an artificial hand by time clustering and Takagi-Sugeno modeling. In: Proceedings of the IEEE International Conference on Fuzzy Systems, Vancouver, BC, Canada, 16–21 July 2006, pp. 291–298 (2006). doi:10.1109/fuzzy.2006.1681728
Iliev, B., Kadmiry, B., Palm, R.: Interpretation of human demonstrations using mirror neuron system principles. In: Proceedings of the 6th IEEE International Conference on Development and Learning, Imperial College London, 11–13 July 2007, pp. 128–133 (2007). doi:10.1109/DEVLRN.2007.4354036
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. SMC-15(1), 116–132 (1985)
Gustafson, D.E., Kessel, W.C.: Fuzzy clustering with a fuzzy covariance matrix. In: Proceedings of the 1979 IEEE CDC, pp. 761–766 (1979)
Palm, R., Stutz, C.: Generation of control sequences for a fuzzy gain scheduler. Int. J. Fuzzy Syst. 5(1), 1–10 (2003)
Palm, R., Driankov, D., Hellendoorn, H.: Model Based Fuzzy Control. Springer, Berlin (1997)
Tegin, J., Ekvall, S., Kragic, D., Wikander, J., Iliev, B.: Demonstration based learning and control for automatic grasping. J. Intell. Serv. Robot. 2(1), 23–30 (2009). doi:10.1007/s11370-008-0026-3
Delson, N., West, H.: Robot programming by human demonstration: adaptation and inconsistency in constrained motion. In: IEEE International Conference on Robotics and Automation, pp. 30–36 (1996). doi:10.1109/ROBOT.1996.503569
Skoglund, A., Iliev, B., Palm, R.: A hand state approach to imitation with a next-state-planner for industrial manipulators. In: Proceedings of the 2008 International Conference on Cognitive Systems, University of Karlsruhe, Karlsruhe, Germany, 2–4 April 2008, pp. 130–137 (2008)
Conditt, M.A., Mussa-Ivaldi, F.A.: Central representation of time during motor learning. Proc. Natl. Acad. Sci. USA 96, 11625–11630 (1999)
Palm, R., Iliev, B.: Segmentation and recognition of human grasps for programming-by-demonstration using time-clustering and fuzzy modeling. In: Proceedings of the IEEE International Conference on Fuzzy Systems, London, UK, 23–26 July 2007
Bullock, D., Grossberg, S.: VITE and FLETE: neural modules for trajectory formation and postural control. In: Hershberger, W.A. (ed.) Volitonal Action, pp. 253–297. Elsevier Science, Amsterdam (1989)
Skoglund, A., Tegin, J., Iliev, B., Palm, R.: Programming-by-demonstration of reach to grasp tasks in hand-state space. In: Proceedings of the 14th International Conference on Advanced Robotics, Munich, Germany, 22–26 June 2009
Skoglund, A.: Programming by demonstration of robot manipulators. PhD thesis, Örebro University, June 2009
Levine, W.S.: The root locus plot. In: Levine, W.S. (ed.) The Control Handbook, pp. 192–198. CRC Press, Boca Raton (1996)
Palm, R., Iliev, B., Kadmiry, B.: Grasp recognition by fuzzy modeling and hidden Markov models (hmm). In: Robot Intelligence. Springer, Berlin (2010)
Tegin, J., Wikander, J., Iliev, B.: A sub €1000 robot hand for grasping—design, simulation and evaluation. In: International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, Coimbra, Portugal, September 2008
Tegin, J., Ekvall, S., Kragic, D., Iliev, B., Wikander, J.: Demonstration based learning and control for automatic grasping. In: International Conference on Advanced Robotics, Jeju, Korea, August 2007
Hersch, M., Billard, A.G.: A biologically-inspired controller for reaching movements. In: Proceedings of the IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, Pisa, pp. 1067–1071 (2006). doi:10.1109/BIOROB.2006.1639233
Acknowledgements
Johan Tegin at Mechatronics Laboratory, at the Royal Institute of Technology, Stockholm, should be acknowledged for providing access to the KTHand.
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Skoglund, A., Iliev, B., Palm, R. (2010). Programming-by-Demonstration of Robot Motions. In: Liu, H., Gu, D., Howlett, R., Liu, Y. (eds) Robot Intelligence. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84996-329-9_1
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DOI: https://doi.org/10.1007/978-1-84996-329-9_1
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