nips nips2010 nips2010-171 nips2010-171-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Silvia Chiappa, Jan R. Peters
Abstract: Many time-series such as human movement data consist of a sequence of basic actions, e.g., forehands and backhands in tennis. Automatically extracting and characterizing such actions is an important problem for a variety of different applications. In this paper, we present a probabilistic segmentation approach in which an observed time-series is modeled as a concatenation of segments corresponding to different basic actions. Each segment is generated through a noisy transformation of one of a few hidden trajectories representing different types of movement, with possible time re-scaling. We analyze three different approximation methods for dealing with model intractability, and demonstrate how the proposed approach can successfully segment table tennis movements recorded using a robot arm as haptic input device. 1
[1] R. Boulic, B. Ulicny, and D. Thalmann. Versatile walk engine. Journal of Game Development, 1(1):29–52, 2004.
[2] S. Calinon, F. Guenter, and A. Billard. On learning, representing and generalizing a task in a humanoid robot. IEEE Transactions on Systems, Man and Cybernetics, Part B, 37(2):286–298, 2007.
[3] G. Celeux and J. Diebolt. The SEM algorithm: A probabilistic teacher algorithm derived from the EM algorithm for the mixture problem. Computational Statistics Quarterly, 2:73–82, 1985.
[4] S. Chiappa. Hidden Markov switching models with explicit regime-duration distribution. Under submission.
[5] S. Chiappa, J. Kober, and J. Peters. Using Bayesian dynamical systems for motion template libraries. In Advances in NIPS 21, pages 297–304, 2009.
[6] A. Coates, P. Abbeel, and A. Y. Ng. Learning for control from multiple demonstrations. In Proceedings of ICML, pages 144–151, 2008.
[7] J. Durbin and S. J. Koopman. Time Series Analysis by State Space Methods. Oxford Univ. Press, 2001.
[8] S. Fr¨ hwirth-Schnatter. Data augmentation and dynamic linear models. Journal of Time-Series u Analysis, 15:183–202, 1994.
[9] A. Ijspeert, J. Nakanishi, and S. Schaal. Learning attractor landscapes for learning motor primitives. In Advances in NIPS 15, pages 1547–1554, 2003.
[10] U. Kersting, P. McAlpine, B. Rosenhahn, H. Seidel, and R. Klette. Marker-less human motion tracking opportunities for field testing in sports. Journal of Biomechanics, 39:S191–S191, 2006.
[11] J. Listgarten, R. M. Neal, S. T. Roweis, and A. Emili. Multiple alignment of continuous time series. In Advances in NIPS 17, pages 817–824, 2005.
[12] J. Listgarten, R. M. Neal, S. T. Roweis, R. Puckrin, and S. Cutler. Bayesian detection of infrequent differences in sets of time series with shared structure. In Advances in NIPS 19, pages 905–912, 2007.
[13] R. McDonnell, S. J˙ .org, J. K. Hodgins, F. N. Newell, and C. O’Sullivan. Evaluating the effect of motion and body shape on the perceived sex of virtual characters. ACM Transactions on Applied Perception, 5(4), 2009. 8
[14] W. Pan and L. Torresani. Unsupervised hierarchical modeling of locomotion styles. In Proceedings of ICML, 2009.
[15] M. Peinado, D. Maupu, D. Raunhardt, D. Meziat, D. Thalmann, and R. Boulic. Full-body avatar control with environment awareness. IEEE Computer Graphics and Applications, 29(3), 2009.
[16] W. Takano, K. Yamane, and Y. Nakamura. Capture database through symbolization, recognition and generation of motion patterns. In Proceedings of ICRA, pages 3092–3097, 2007.
[17] B. Williams, M. Toussaint, and A. Storkey. Modelling motion primitives and their timing in biologically executed movements. In Advances in NIPS 20, pages 1609–1616, 2008.
[18] K. Yamane and J. K. Hodgins. Simultaneous tracking and balancing of humanoid robots for imitating human motion capture data. In Proceedings of IROS, pages 2510–2517, 2009. 9