INTEGRATION OF MULTIPLE SENSOR SPACES WITH LIMITED SENSING RANGE AND REDUNDANCY

Yuichi Kobayashi, Eisuke Kurita, and Manabu Gouko

References

  1. [1] R. Sutton and A. Barto, Reinforcement learning (Cambridge: MIT Press, 1998).
  2. [2] J. Morimoto and K. Doya, Acquisition of stand-up behaviour by a real robot using hierarchical reinforcement learning, Robotics and Autonomous Systems, 36(1), 2001, 37-51.
  3. [3] J. Nakanishi, J. Morimoto, G. Endo, G. Cheng, S. Schaal, and M. Kawato, Learning from demonstration and adaptation of biped locomotion, Robotics and Autonomous Systems, 47, 2004, 79–91.
  4. [4] H. Kimura, T. Yamashita, and S. Kobayashi, Reinforcement learning of walking behaviour for a four-legged robot, Proc. of IEEE Conf. on Decision and Control, Orlando, Florida, USA, 2001, 411–416.
  5. [5] M. Asada, K. MacDorman, H. Ishiguro, and Y. Kuniyoshi, Cognitive developmental robotics as a new paradigm for the design of humanoid robots, Robotics and Autonomous Systems, 37, 2001, 185–193.
  6. [6] A. Stoytchev, Some basic principles of developmental robotics, IEEE Transactions on Autonomous Mental Development, 1(2), 2009, 122–130.
  7. [7] R.C. Luo, C.C. Yih, and K.L. Su, Multisensor fusion and integration: Approaches, applications, and future research directions, IEEE Sensors Journal, 2(2), 2002, 107–119.
  8. [8] R.E. Kalman, A new approach to linear filtering and prediction problems, Transactions of the ASME-Journal of Basic Engineering, 82 (Series D), 1960, 35–45.
  9. [9] D. Fox, S. Thrun, W. Burgard, and F. Dellaert, Particle filters for mobile robot localization, in A. Doucet, N. de Freitas, and N. Gordon (eds.), Sequential Monte Carlo methods in practice, (New York: Springer Verlag, 2001), 499–516.
  10. [10] A. Elfes, Using occupancy grids for mobile robot perception and navigation, Computer, 22(6), 1989, 46–57.
  11. [11] J.F. Ferreira, J. Lobo, and J. Dias, Bayesian real-time perception algorithms on GPU–real-time implementation of Bayesian models for multimodal perception using CUDA, Journal of Real-Time Image Processing, Part II Special issue on: Parallel Computing for Real-Time Image Processing, 6(3), 2011, 171–186.
  12. [12] J.F. Ferreira, J.A. Prado, J. Lobo, and J. Dias, Multimodal active exploration using a bayesian approach, 14th IASTED Intl. Conf. in Robotics and Applications, Cambridge, MA, 2009.
  13. [13] S. Thrun, W. Burgard, and D. Fox, Probabilistic robotics, (New York, USA: MIT Press, 2005).
  14. [14] L.P. Kaelbling, M.L. Littman, and A.R. Cassandra, Planning and acting in partially observable stochastic domains, Artificial Intelligence, 101, 1998, 99–134.
  15. [15] R.A. McCallum, Instance-based utile distinctions for reinforcement learning with hidden state, Proc. of the 12th Int. Machine Learning Conf., Tahoe City, California, USA, 1995.
  16. [16] D. Wierstra, A. Foerster, J. Peters, and J. Schmidhuber, Solving deep memory POMDPs with recurrent policy gradients, Proc. of Intl. Conf. on Artificial Neural Networks ICANN’07, Porto, Portugal, 2007.
  17. [17] Z.W. Luo and M. Ito, Diffusion-based learning theory for organizing visuo-motor coordination, Biological Cybernetics, 79, 1998, 279–289.
  18. [18] M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active contour models, International Journal of Computer Vision, 1(4), 1988, 321–331.
  19. [19] K. Fukunaga, Introduction to statistical pattern recognition (San Diego: Academic Press, 1990).
  20. [20] B. Schölkopf, A. Smola, and K. Müller, Nonlinear component analysis as a kernel eigenvalue problem, Neural Computation, 10 (5), 1998, 1299–1319.
  21. [21] J.B. Tenenbaum, V. de Silva, and J.C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science, 290(5500), 2000, 2319–2323.

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