Composing Dynamical Systems to Realize Dynamic Robotic Dancing

Chapter
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 107)

Abstract

This paper presents a methodology for the composition of complex dynamic behaviors in legged robots, and illustrates these concepts to experimentally achieve robotic dancing. Inspired by principles from dynamic locomotion, we begin by constructing controllers that drive a collection of virtual constraints to zero; this creates a low-dimensional representation of the bipedal robot. Given any two poses of the robot, we utilize this low-dimensional representation to connect these poses through a dynamic transition. The end result is a meta-dynamical system that describes a series of poses (indexed by the vertices of a graph) together with dynamic transitions (indexed by the edges) connecting these poses. These formalisms are illustrated in the case of dynamic dancing; a collection of ten poses are connected through dynamic transitions obtained via virtual constraints, and transitions through the graph are synchronized with music tempo. The resulting meta-dynamical system is realized experimentally on the bipedal robot AMBER 2 yielding dynamic robotic dancing.

References

  1. 1.
    Dynamic Robotic Dancing on AMBER 2. http://youtu.be/IwR9XvojXWo
  2. 2.
    Ames, A.D.: First steps toward automatically generating bipedal robotic walking from human data. In: Robotic Motion and Control 2011, vol. 422. Springer (2012)Google Scholar
  3. 3.
    Ames, A.D., Cousineau, E.A., Powell, M.J.: Dynamically stable robotic walking with NAO via human-inspired hybrid zero dynamics. In: Hybrid Systems: Computation and Control, Beijing, China (2012)Google Scholar
  4. 4.
    Aucouturier, J.-J.: Cheek to chip: dancing robots and Ai’s future. IEEE Intell. Syst. 23(2), 74–84 (2008)CrossRefGoogle Scholar
  5. 5.
    Bennewitz, M., Pastrana, J., Burgard, W.: Active localization of persons with a mobile robot based on learned motion behaviors. In: Proceedings of the 3rd Workshop on Self Organization of Adaptive Behavior (SOAVE) (2004)Google Scholar
  6. 6.
    Brock, O., Khatib, O., Viji, S.: Task-consistent obstacle avoidance and motion behavior for mobile manipulation. In: Proceedings of the ICRA’02, 2002 IEEE International Conference on Robotics and Automation, vol. 1, pp. 388–393 (2002)Google Scholar
  7. 7.
    Ellis, D.P.W.: Beat tracking by dynamic programming. J. New Music Res. 36(1), 51–60 (2007)CrossRefGoogle Scholar
  8. 8.
    Ijspeert, A.J., Nakanishi, J., Schaal, S.: Learning attractor landscapes for learning motor primitives. In: Advances in NIPS, pp. 1523–1530. MIT Press (2003)Google Scholar
  9. 9.
    Jiang, X., Motai, Y.: Learning by observation of robotic tasks using on-line pca-based eigen behavior. In: Proceedings. 2005 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2005. pp. 391–396. IEEE (2005)Google Scholar
  10. 10.
    Johnson, A.M., Koditschek, D.E.: Legged self-manipulation. IEEE Access 1, 310–334 (2013)CrossRefGoogle Scholar
  11. 11.
    Khatib, O., Sentis, L., Park, J., Warren, J.: Whole-body dynamic behavior and control of human-like robots. Int. J. Humanoid Robot. 1(01), 29–43 (2004)CrossRefGoogle Scholar
  12. 12.
    Michalowski, M.P., Sabanovic, S., Kozima H.: A dancing robot for rhythmic social interaction. In: Proceedings of the ACM/IEEE International Conference on Human-robot Interaction, HRI ’07, pp. 89–96, ACM, New York (2007)Google Scholar
  13. 13.
    Murray, R.M., Li, Z., Sastry, S.S.: A Mathematical Introduction to Robotic Manipulation. CRC Press, Boca Raton (1994)MATHGoogle Scholar
  14. 14.
    Nakaoka, S., Nakazawa, A., Yokoi, K., Ikeuchi, K.: Leg motion primitives for a dancing humanoid robot. In: Proceedings of the ICRA’04. vol. 1, pp. 610–615, IEEE (2004)Google Scholar
  15. 15.
    Park, H., Ramezani, A., Grizzle, J.W.: a Finite-state machine for accommodating unexpected large ground-height variations in bipedal robot walking. IEEE Trans. Robot. 29(2), 331–345 (2013)CrossRefGoogle Scholar
  16. 16.
    Powell, M.J., Zhao, H., Ames, A.D.: Motion primitives for human-inspired bipedal robotic locomotion: walking and stair climbing. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 543–549 (2012)Google Scholar
  17. 17.
    Sastry, S.S.: Nonlinear Systems: Analysis Stability and Control. Springer, New York (1999)CrossRefMATHGoogle Scholar
  18. 18.
    Sreenath, K., Park, H., Poulakakis, I., Grizzle, J.W.: A compliant hybrid zero dynamics controller for stable, efficient and fast bipedal walking on MABEL. Int. J. Robot. Res. 30(9), 1170–1193 (2011)CrossRefGoogle Scholar
  19. 19.
    Westervelt, E.R., Grizzle, J.W., Chevallereau, C., Choi, J.H., Morris, B.: Feedback Control of Dynamic Bipedal Robot Locomotion. CRC Press, Boca Raton (2007)CrossRefGoogle Scholar
  20. 20.
    Yadukumar, S.N., Pasupuleti, M., Ames, A.D.: From formal methods to algorithmic implementation of human inspired control on bipedal robots. In: Tenth International Workshop on the Algorithmic Foundations of Robotics, Cambridge, MA (2012)Google Scholar
  21. 21.
    Zhao, H., Ma, W., Zeagler, W.B., Ames, A.D.: Human-inspired multi-contact locomotion with AMBER2. In: International Conference on Cyber Physical Systems (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shishir Kolathaya
    • 1
  • Wen-Loong Ma
    • 1
  • Aaron D. Ames
    • 1
  1. 1.Texas A& M UniversityCollege StationUSA

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