Hostname: page-component-77f85d65b8-jkvpf Total loading time: 0 Render date: 2026-04-22T14:03:24.461Z Has data issue: false hasContentIssue false
  • English
  • Français

Action sequencing using dynamic movement primitives

Published online by Cambridge University Press:  05 October 2011

Bojan Nemec  and
Aleš Ude
Bojan Nemec*
Affiliation:
Department of Automatics, Biocybernetics, and Robotics, Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia E-mail: ales.ude@ijs.si
Aleš Ude
Affiliation:
Department of Automatics, Biocybernetics, and Robotics, Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia E-mail: ales.ude@ijs.si
*
*Corresponding author. E-mail: bojan.nemec@ijs.si
Get access Rights & Permissions [Opens in a new window]

Summary

General-purpose autonomous robots must have the ability to combine the available sensorimotor knowledge in order to solve more complex tasks. Such knowledge is often given in the form of movement primitives. In this paper, we investigate the problem of sequencing of movement primitives. We selected nonlinear dynamic systems as the underlying sensorimotor representation because they provide a powerful machinery for the specification of primitive movements. We propose two new methodologies which both ensure that consecutive movement primitives are joined together in a continuous way (up to second-order derivatives). The first is based on proper initialization of the third-order dynamic motion primitives and the second uses online Gaussian kernel functions modification of the second-order dynamic motion primitives. Both methodologies were validated by simulation and by experimentally using a Mitsubishi PA-10 articulated robot arm. Experiments comprehend pouring, table wiping, and carrying a glass of liquid.

Keywords

Trajectory generationDynamic movement primitivesImitation learning

Information

Type
Articles
Information
Robotica , Volume 30 , Issue 5 , September 2012 , pp. 837 - 846
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

1.Bentivegna, D. C., Atkeson, C. G., Ude, A. and Cheng, G., “Learning to act from observation and practice,” Int. J. Humanoid Robot. 1 (4), 585–611 (2004).Google Scholar
2.Dillmann, R., “Teaching and learning of robot tasks via observation of human performance,” Robot. Auton. Syst. 47 (2), 109–116 (2008).CrossRefGoogle Scholar
3.Wolpert, D. M. and Kawato, M., “Multiple paired forward and inverse models for motor control,” Neural Netw. 11, 1317–1329 (1998).Google Scholar
4.Matarić, M. J., Sensory Motor Primitives as a Basis for Imitation: Linking Perception to Action and Biology to Robotics (MIT Press, 2002).Google Scholar
5.Ijspeert, A. J., Nakanishi, J. and Schaal, S., “Learning Rhythmic Movements by Demonstration Using Nonlinear Oscillators,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland (2002) pp. 958–963.Google Scholar
6.Ijspeert, A. J., Nakanishi, J. and Schaal, S., “Movement Imitation with Nonlinear Dynamical Systems in Humanoid Robots,” Proceedings of the IEEE International Conference on Robotics and Automation, Washington, DC (2002) pp. 1398–1403.Google Scholar
7.Ijspeert, A. J., Nakanishi, J. and Schaal, S., “Learning Attractor Landscapes for Learning Motor Primitives,” In: Advances in Neural Information Processing Systems 15 (Becker, S., Thrun, S. and Obermayer, K., eds.) MIT Press, Cambridge, MA, 2003) pp. 1547–1554.Google Scholar
8.Schaal, S., Peters, J., Nakanishi, J. and Ijspeert, A., “Learning Movement Primitives,” In: Robotics Research: The Eleventh International Symposium (Dario, P. and Chatila, R., eds.) (Springer, Berlin, Heidelberg, 2005) pp. 561–572.CrossRefGoogle Scholar
9.Pastor, P., Hoffmann, H., Asfour, T. and Schaal, S., “Learning and Generalization of Motor Skills by Learning from Demonstration,” Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan (2009) pp. 763–768.Google Scholar
10.Peters, J. and Schaal, S., “Reinforcement learning of motor skills with policy gradients,” Neural Netw. 21, 682–697 (2008).Google Scholar
11.Kober, J. and Peters, J., “Learning Motor Primitives for Robotics,” ICRA'09: Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan (2009) pp. 2112–2118.Google Scholar
12.Park, D.-H., Hoffmann, H., Pastor, P. and Schaal, S., “Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields,” Proceedings of the 8th IEEE-RAS International Conference on Humanoid Robots (2008) pp. 91–98.Google Scholar
13.Hoffmann, H., Pastor, P., Park, D. and Schaal, S., “Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance,” Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan (2009) pp. 2587–2592.Google Scholar
14.Schaal, S., Mohajerian, P. and Ijspeert, A., “Dynamics systems vs. optimal control—a unifying view,” Prog. Brain Res. 165 (6), 425–445 (2007).CrossRefGoogle ScholarPubMed
15.Nemec, B., Tamosiunaite, M., Worgotter, F. and Ude, A., “Task Adaptation through Exploration and Action Sequencing”, Proceedings of the 9th IEEE-RAS International Conference on Humanoid Robots, Paris, France (2009) pp. 610–616.Google Scholar
16.Nemec, B., Zorko, M. and Žlajpah, L., “Learning of a Ball-in-a-Cup Playing Robot,” Proceedings of the 19th IEEE International Workshop on Robotics in Alpe-Adria-Danube Region, Budapest, Hungary (2010) pp. 297–301.Google Scholar
17.Gams, A., Ijspeert, A., Schaal, S. and Lenarcic, J., “On-line learning and modulation of periodic movements with nonlinear dynamical systems,” Auton. Robots 27 (1), 3–23 (2009).Google Scholar
18.Ude, A., Gams, A., Asfour, T. and Morimoto, J., “Task-specific generalization of discrete and periodic dynamic movement primitives,” IEEE Trans. Robot. 26 (5), 800–815 (2010).CrossRefGoogle Scholar
19.Atkeson, C. G., Moore, A. W. and Schaal, S., “Locally weighted learning,” AI Review 11, 11–73 (1997).Google Scholar
20.Bioengineering, BTS. High frequency digital system for biomechanical motion analysis. http://www.btsbioengineering.com/Media/Brochure/assets/bts_smartd_a40308uk.pdf.Google Scholar
21.Kulič, D., Takano, W. and Nakamura, Y., “Online segmentation and clustering from continuous observation of whole body motions,” IEEE Trans. Robot. 25 (5), 1158–1166 (2009).CrossRefGoogle Scholar
22.Krueger, V., Herzog, L. D., Baby, S., Ude, A. and Kragic, D, “Learning actions from observation,” IEEE Robot. Autom. Mag. 17 (2), 30–43 (2010).Google Scholar
23.Fod, A., Matarič, M. J. and Jenkins, O. C., “Automated derivation of primitives for movement classification,” Auton. Robots 12 (1), 39–54 (2002).Google Scholar