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Planning-Space Shift Motion Generation: Variable-space Motion Planning Toward Flexible Extension of Body Schema

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Abstract

To improve the flexibility of robotic learning, it is important to realize an ability to generate a hierarchical structure. This paper proposes a learning framework which can dynamically change the planning space depending on the structure of tasks. Synchronous motion information is utilized to generate ’modes’ and hierarchical structure of the controller is constructed based on the modes. This enables efficient planning and control in low-dimensional planning space, though the dimension of the total state space is in general very high. Three types of object manipulation tasks are tested as applications, where an object is found and used as a tool (or as a part of the body) to extend the ability of the robot. The proposed framework is expected to be a basic learning model to account for body schema acquisition including tool affordances.

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References

  1. Asada, M., MacDorman, K., Ishiguro, H., Kuniyoshi, Y.: Cognitive developmental robotics as a new paradigm for the design of humanoid robots. Robot. Auton. Syst. 37, 185–193 (2001)

    Article  MATH  Google Scholar 

  2. Atkeson, C.G., Moore, A.W., Schall, S.: Locally weighted learning. Artif. Intell. Rev. 11, 11–73 (1997)

    Article  Google Scholar 

  3. Bemporad, A., Morari, M., Dua, V., Pistikopoulos, E.: The explicit linear quadratic regulator for constrained systems. Automatica 38(1), 3–20 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  4. Blakemore, S.J., Wolpert, D.M., Frith, C.D.: Abnormalities in the awareness of action. Trends Cogn. Sci. 6(6), 237–242 (2002)

    Article  Google Scholar 

  5. Brooks, R.A.: A robust layered control system for a mobile robot. IEEE J. Robot. Autom. RA-2, 253–262 (1986)

    Google Scholar 

  6. Dayan, P., Hinton, G.E.: Feudal reinforcement learning. In: Giles, C.L., Hanson, S.J., Cowan, J.D. (eds.) Advances in Neural Information Processing Systems, vol. 5, pp. 271–278 (1993)

  7. Demiris, Y., Dearden, A.: From motor babbling to hierarchical learning by imitation: a robot developmental pathway. In: Berthouse, L., et al. (eds.) Proc. of the Fifth Int. Workshop on Epigenetic Robotics, pp. 31–37. Japan (2005)

  8. Demiris, Y., Meltzoff, A.: The robot in the crib: a developmental analysis of imitation skills in infants and robots. Infant Child Dev. 17, 43–53 (2008)

    Article  Google Scholar 

  9. Drescher, G.L.: Made-Up Minds: A Constructivist Approach to Artificial Intelligence. MIT (1991)

  10. Gibson, J.J.: The theory of affordances. In: Perceiving, Acting, and Knowing: Toward Ecological Psychology, pp. 62–82. Lawrence Erlbaum, NJ (1977)

    Google Scholar 

  11. Hauskrecht, M., Meuleau, N., Boutilier, C., Kaelbling, L.P., Dean, T.: Hierarchical solution of markov decision processes using macro-actions. In: Proc. of 14th Annual Conference on Uncertainty in Artificial Intelligence, pp. 220–222 (1998)

  12. Khalil, H.K. (ed): Nonlinear Systems. Prentice Hall (2001)

  13. Kuniyoshi, Y., Sangawa, S.: Early motor development from partially ordered neural-body dynamics: experiments with a cortico-spinal musculo-skeletal model. Biol. Cybern. 95, 589–605 (2006)

    Article  MATH  Google Scholar 

  14. Lavalle, S.M.: Planning Algorithms. Cambridge University Press (2006)

  15. Lungarella, M., Metta, G., Pfeifer, R., Sandini, G.: Developmental robotics: a survey. Connect. Sci. 15(4), 151–190 (2003)

    Article  Google Scholar 

  16. Mangasarian, O.L., Musicant, D.R.: Lagrangian support vector machines. J. Mach. Learn. Res. 1, 161–177 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  17. Maravita, A., Iriki, A.: Tools for the body (schema). Trends Cogn. Sci. 8(2), 79–86 (2004)

    Article  Google Scholar 

  18. Metta, G., Fitzpatrick, P.: Early integration of vision and manipulation. Adapt. Behav. 11(2), 109–128 (2003)

    Article  Google Scholar 

  19. Michael Kass, A.W., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)

    Article  Google Scholar 

  20. Miyamoto, H., Morimoto, J., Doya, K., Kawato, M.: Reinforcement learning with via-point representation. Neural Netw. 17, 299–305 (2004)

    Article  MATH  Google Scholar 

  21. Nabeshima, C., Kuniyoshi, Y., Lungarella, M.: Adaptive body schema for robotic tool use. Adv. Robot. 20(10), 1105–1126 (2006)

    Article  Google Scholar 

  22. Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7, 308–313 (1965)

    MATH  Google Scholar 

  23. Oyama, E., Agah, A., MacDorman, K.F., Maeda, T., Tachi, S.: A modular neural network architecture for inverse kinematics model learning. Neurocomputing 38–40, 797–805 (2001)

    Article  Google Scholar 

  24. Ratitch B., Precup D.: Using MDP Characteristics to Guide Exploration in Reinforcement Learning. Springer, Berlin (2003)

    Google Scholar 

  25. van der Schaft, A., Schumacher, H.: An Introduction to Hybrid Dynamical Systems. Springer (2000)

  26. Siméon, T., Laumond, J.P., Cortés, J., Sahbani, A.: Manipulation planning with probabilistic roadmaps. Int. J. Rob. Res. 23(7–8), 729–746 (2004)

    Article  Google Scholar 

  27. Stoytchev, A.: Behavior-grounded representation of tool affordances. In: Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 3071–3076. Barcelona, Spain (2005)

  28. Stoytchev, A.: Toward video-guided robot behaviors. In: Proc. of the Seventh International Conference on Epigenetic Robotics, pp. 165–172. Rutgers University, NJ (2007)

    Google Scholar 

  29. Vapnik, V.N.: The Nature of Statictical Learning Theory. Springer (1995)

  30. Wolpert, D.M., Kawato, M.: Multiple paired forward and inverse models for motor control. Neural Netw. 11, 1317–1329 (1998)

    Article  Google Scholar 

  31. Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  32. Yoshikawa, Y., Hosoda, K., Asada, M.: Does the invariance in multi-modalities represent the body scheme?—A case study with vision and proprioception—. In: Proc. of the 2nd Int. Symp. on Adaptive Motion of Animals and Machines, vol. SaP-II-1 (2003)

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Authors and Affiliations

  1. Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, Japan

    Yuichi Kobayashi

  2. RIKEN-TRI Collaboration Center for Human-Interactive Robot Research (RTC), Nagoya University, Shimoshidami, Anagahora, Nagoya, Japan

    Shigeyuki Hosoe

Authors
  1. Yuichi Kobayashi
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  2. Shigeyuki Hosoe
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Correspondence to Yuichi Kobayashi.

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Kobayashi, Y., Hosoe, S. Planning-Space Shift Motion Generation: Variable-space Motion Planning Toward Flexible Extension of Body Schema. J Intell Robot Syst 62, 467–500 (2011). https://doi.org/10.1007/s10846-010-9465-0

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  • DOI: https://doi.org/10.1007/s10846-010-9465-0

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