Motion Synthesis through Randomized Exploration on Submanifolds of Configuration Space

  • Ioannis Havoutis
  • Subramanian Ramamoorthy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5949)


Motion synthesis for humanoid robot behaviours is made difficult by the combination of task space, joint space and kinodynamic constraints that define realisability. Solving these problems by general purpose methods such as sampling based motion planning has involved significant computational complexity, and has also required specialised heuristics to handle constraints. In this paper we propose an approach to incorporate specifications and constraints as a bias in the exploration process of such planning algorithms. We present a general approach to solving this problem wherein a subspace, of the configuration space and consisting of poses involved in a specific task, is identified in the form of a nonlinear manifold, which is in turn used to focus the exploration of a sampling based motion planning algorithm. This allows us to solve the motion planning problem so that we synthesize previously unseen paths for novel goals in a way that is strongly biased by known good or feasible paths, e.g., from human demonstration. We demonstrate this result with a simulated humanoid robot performing a number of bipedal tasks.


Motion Planning Humanoid Robot Geodesic Distance Task Space Manifold Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006)MATHCrossRefGoogle Scholar
  2. 2.
    Full, R., Koditschek, D.: Templates and anchors: neuromechanical hypotheses of legged locomotion on land. J. Exp. Biol. 202(23), 3325–3332 (1999)Google Scholar
  3. 3.
    Gutfreund, Y., Flash, T., Yarom, Y., Fiorito, G., Segev, I., Hochner, B.: Organization of Octopus Arm Movements: A Model System for Studying the Control of Flexible Arms. J. Neurosci. 16(22), 7297–7307 (1996)Google Scholar
  4. 4.
    Ramamoorthy, S., Kuipers, B.J.: Qualitative hybrid control of dynamic bipedal walking. In: Proceedings of Robotics: Science and Systems, Philadelphia, USA (August 2006)Google Scholar
  5. 5.
    Ramamoorthy, S., Kuipers, B.J.: Trajectory generation for dynamic bipedal walking through qualitative model based manifold learning. In: IEEE International Conference on Robotics and Automation (ICRA), May 2008, pp. 359–366 (2008)Google Scholar
  6. 6.
    Isto, P., Saha, M.: A slicing connection strategy for constructing prms in high-dimensional cspaces. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, May 2006, pp. 1249–1254 (2006)Google Scholar
  7. 7.
    Safonova, A., Hodgins, J.K., Pollard, N.S.: Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. ACM Trans. Graph. 23(3), 514–521 (2004)CrossRefGoogle Scholar
  8. 8.
    Stilman, M.: Task constrained motion planning in robot joint space. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007, 29 November 2 (2007)Google Scholar
  9. 9.
    Yao, Z., Gupta, K.: Path planning with general end-effector constraints: using task space to guide configuration space search. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005), August 2005, pp. 1875–1880 (2005)Google Scholar
  10. 10.
    Bretl, T., Lall, S., Latombe, J.C., Rock, S.: Multi-step motion planning for free-climbing robots. In: WAFR, pp. 1–16 (2004)Google Scholar
  11. 11.
    James, J., Kuffner, J., Kagami, S., Nishiwaki, K., Inaba, M., Inoue, H.: Dynamically-stable motion planning for humanoid robots. Auton. Robots 12(1), 105–118 (2002)MATHCrossRefGoogle Scholar
  12. 12.
    Nakamura, Y., Yamane, K.: Interactive motion generation of humanoid robots via dynamics filter. In: Proc. of First IEEE-RAS Int. Conf. on Humanoid Robots (2000)Google Scholar
  13. 13.
    Beaudoin, P., van de Panne, M., Poulin, P., Coros, S.: Motion-motif graphs. In: Symposium on Computer Animation 2008 (July 2008)Google Scholar
  14. 14.
    LaValle, S.M., Kuffner, J.J.: Randomized kinodynamic planning. The International Journal of Robotics Research 20(5), 378–400 (2001)CrossRefGoogle Scholar
  15. 15.
    Vijayakumar, S., D’souza, A., Schaal, S.: Incremental online learning in high dimensions. Neural Comput. 17(12), 2602–2634 (2005)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Bitzer, S., Havoutis, I., Vijayakumar, S.: Synthesising novel movements through latent space modulation of scalable control policies. In: Asada, M., Hallam, J.C.T., Meyer, J.-A., Tani, J. (eds.) SAB 2008. LNCS (LNAI), vol. 5040, pp. 199–209. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Jenkins, O.C., Mataric, M.J.: A spatio-temporal extension to isomap nonlinear dimension reduction. In: International Conference on Machine Learning (ICML), pp. 441–448 (2004)Google Scholar
  18. 18.
    Wang, J.M., Fleet, D.J., Hertzmann, A.: Gaussian process dynamical models for human motion. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 283–298 (2008)CrossRefGoogle Scholar
  19. 19.
    Urtasun, R., Fleet, D.J., Geiger, A., Popovic, J., Darrell, T.J., Lawrence, N.D.: Topologically-constrained latent variable models. In: Proceedings of the 25th international Conference on Machine Learning, Helsinki, Finland, July 5-9, pp. 1080–1087. ACM, New York (2008)CrossRefGoogle Scholar
  20. 20.
    Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning. Springer, Heidelberg (2001)MATHGoogle Scholar
  21. 21.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  22. 22.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  23. 23.
    Dollár, P., Rabaud, V., Belongie, S.: Non-isometric manifold learning: Analysis and an algorithm. In: ICML (June 2007)Google Scholar
  24. 24.
    Dollár, P., Rabaud, V., Belongie, S.: Learning to traverse image manifolds. In: NIPS (December 2006)Google Scholar
  25. 25.
    Blake, A., Isard, M.: Active Contours. Springer, Heidelberg (1998)Google Scholar
  26. 26.
    Michel, O.: Webots: Professional mobile robot simulation. Journal of Advanced Robotics Systems 1(1), 39–42 (2004)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ioannis Havoutis
    • 1
  • Subramanian Ramamoorthy
    • 1
  1. 1.Institute of Perception, Action and Behaviour, School of InformaticsUniversity of EdinburghEdinburghUK

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