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Imitating human acceleration of a gyroscopic device

Published online by Cambridge University Press:  26 February 2007

Andrej Gams ,
Leon Žlajpah  and
Jadran Lenarčič
Andrej Gams*
Affiliation:
Robotics Laboratory, Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia.
Leon Žlajpah
Affiliation:
Robotics Laboratory, Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia.
Jadran Lenarčič
Affiliation:
Robotics Laboratory, Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia.
*
*Corresponding author. E-mail: andrej.gams@ijs.si
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Summary

Spinning up a Power®ball—a hand-held gyroscopic toy or exerciser that exhibits rotor spin-up when applying appropriate torque to its casing—is a fairly easy task for a human but rather complex to perform with a robot. To accomplish the task of spinning up the rotor of the Power®ball with a robot, we measured the motion of a human and identified the conditions an individual uses for a successful spin-up. Several control approaches were applied to the device mainly using feedback information from the velocity counter and force/torque sensor to synchronize the torque exerted by the device and the motion of the robot. Best human imitation was achieved with two modified learning methods with highest rotor speeds in excess of 1480 rad/s, rating among top 100 world Power®ball players.

Keywords

Rhythmic motionSynchronizationGyroscopic deviceImitation

Information

Type
Article
Information
Robotica , Volume 25 , Issue 4 , July 2007 , pp. 501 - 509
Copyright
Copyright © Cambridge University Press 2007

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References

1. Ijspeert, A. J., Nakanishi, J. and Schall, S., “Learning Rhytmic Movements by Demonstration Using Nonlinear Oscillators,” Proceeedings of the 20002 IEEE/RJS International Conference on Intelligent Robots and Systems, Laussane, Switzerland (2002) pp. 958–963.Google Scholar
2. 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, USA (2002) pp. 1398–1403.Google Scholar
3. Žlajpah, L., “Robotic yo-yo: modelling and control strategies,” Robotica 24 (2), 211–220 (2006).CrossRefGoogle Scholar
4. Ijspeert, A. J., Nakanishi, J. and Schaal, S., “Learning Attractor Landscapes for Learning Motor Primitives,” Proceedings of the Advances in Neural Information Processing Systems 15 (Becker, S., Thrun, S. and Obermayer, K., eds.) (2002) pp. 1547–1554.Google Scholar
5. Ijspeert, A. J., Nakanishi, J. and Schall, S., “Trajectory Format-ion for Imitation With Nonlinear Dynamical Systems,” Proceedings of the IEEE/RJS International Conference on Intelligent Robots and Systems (2001) pp. 752–757.Google Scholar
6. Pongas, D., Billard, A. and Schaal, S., “Rapid Synchronization and Accurate Phase-Locking of Rhythmic Motor Primitives,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Edmonton, Canada (2005) pp. 2911–2916.Google Scholar
7. Schaal, S. and Atkeson, C. G., “An implementation of mem-ory-based learning,” Control Syst. Mag. 14 (1), 57–71 (1994).Google Scholar
8. Schaal, S. and Atkeson, C. G., “Open Loop Stable Control Strategies for Robot Juggling,” Proceedings of the IEEE Conference on Robotics and Automation, Atlanta, Georgia (1993) pp. 913–918.Google Scholar
9. Schaal, S., Sternad, D. and Atkeson, C. G., “One-handed juggling: a dynamical approach to a rhytmic movement task,” J. Motor Behav. 28 (2), 165–183 (1996).CrossRefGoogle Scholar
10. Gulick, D. W. and O'Reilly, O. M., “On the dynamics of the Dynabee,” J. Appl. Mech. 67, 321–325 (2000).Google Scholar
11. Heyda, P. G., “Roller ball dynamics revisited,” Am. J. Phys. 70 (10), 1049–1051 (2002).Google Scholar
12. Mishler, A. L., “Gyroscopic Device,” U.S. Patent 3726146 (1973).Google Scholar
13. Curk, B., “Robotiziran Powerball®,” Proceedings of Zbornik ERK 2006 Zvezek B, Portorož, Slovenija (2006) pp. 167–170.Google Scholar
14. Omrčen, D. and Nemec, B., “Measuring knee movement using an industrial robot—gravity compensation for the automatic gripper,” Strojniški vestnik 48 (2), 87–98 (2002).Google Scholar