Summary
This paper describes a novel approach for incremental learning of motion pattern primitives through long-term observation of human motion. Human motion patterns are abstracted into a stochastic model representation, which can be used for both subsequent motion recognition and generation. The model size is adaptable based on the discrimination requirements in the associated region of the current knowledge base. As new motion patterns are observed, they are incrementally grouped together based on their relative distance in the model space. The resulting representation of the knowledge domain is a tree structure, with specialized motions at the tree leaves, and generalized motions closer to the root. Tests with motion capture data for a variety of motion primitives demonstrate the efficacy of the algorithm.
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References
Bentivegna, D.C., Atkeson, C.G., Cheng, G.: Learning similar tasks from observation and practice. In: International Conference on Intelligent Robots and Systems, pp. 2677–2683 (2006)
Billard, A., Calinon, S., Guenter, F.: Discriminative and adaptive imitation in uni-manual and bi-manual tasks. Robotics and Autonomous Systems 54, 370–384 (2006)
Breazeal, C., Scassellati, B.: Robots that imitate humans. Trends in Cognitive Sciences 6(11), 481–487 (2002)
Diday, E., Govaert, G.: Classification automatique avec distances adaptives. R.A.I.R.O. Informatique Computer Science 11(4), 329–349 (1977)
Diday, E., Simon, J.C.: Clustering analysis. In: Fu, K.S. (ed.) Digital Pattern Recognition, pp. 47–92. Springer, Heidelberg (1976)
Ghahramani, Z., Jordan, M.I.: Factorial hidden markov models. Machine Learning 29, 245–273 (1997)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Computing Surveys 31(3), 264–323 (1999)
Kadone, H., Nakamura, Y.: Symbolic memory for humanoid robots using hierarchical bifurcations of attractors in nonmonotonic neural networks. In: International Conference on Intelligent Robots and Systems, pp. 2900–2905 (2005)
Kadone, H., Nakamura, Y.: Segmentation, memorization, recognition and abstraction of humanoid motions based on correlations and associative memory. In: IEEE-RAS International Conference on Humanoid Robots, pp. 1–6 (2006)
Kulić, D., Takano, W., Nakamura, Y.: Incremental on-line hierarchical clustering of whole body motion patterns. In: IEEE International Symposium on Robot and Human Interactive Communication (2007)
Kulić, D., Takano, W., Nakamura, Y.: Representability of human motions by factorial hidden markov models. In: IEEE International Conference on Intelligent Robots and Systems (2007) (to appear)
Kurihara, K., Hoshino, S., Yamane, K., Nakamura, Y.: Optical motion capture system with pan-tilt camera tracking and realtime data processing. In: IEEE International Conference on Robotics and Automation, vol. 2, pp. 1241–1248 (2002)
Nicolescu, M.N., Matarić, M.J.: Task learning through imitation and human-robot interaction. In: Dautenhahn, K., Nehaniv, C. (eds.) Imitation and social learning in robots, humans and animals: behavioral, social and communicative dimensions, Cambridge University Press, Cambridge (2005)
Ogata, T., Sugano, S., Tani, J.: Open-end human-robot interaction from the dynamical systems perspective: mutual adaptation and incremental learning. Advanced Robotics 19, 651–670 (2005)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Schaal, S., Ijspeert, A., Billard, A.: Computational approaches to motor learning by imitation. Philosophical Transactions of the Royal Society of London B: Biological Sciences 358, 537–547 (2003)
Takano, W., Nakamura, Y.: Humanoid robot’s autonomous acquisition of proto-symbols through motion segmentation. In: IEEE-RAS International Conference on Humanoid Robots, pp. 425–431 (2006)
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Kulić, D., Takano, W., Nakamura, Y. (2010). Towards Lifelong Learning and Organization of Whole Body Motion Patterns. In: Kaneko, M., Nakamura, Y. (eds) Robotics Research. Springer Tracts in Advanced Robotics, vol 66. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14743-2_8
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DOI: https://doi.org/10.1007/978-3-642-14743-2_8
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