Modeling and Learning Behaviors

  • Dizan Vasquez
  • Christian Laugier
Reference work entry


In order to safely navigate in a dynamic environment, a robot requires to know how the objects populating it will move in the future. Since this knowledge is seldom available, it is necessary to resort to motion prediction algorithms. Due to the difficulty of modeling the various factors that determine motion (e.g., internal state, perception), this is often done by applying machine-learning techniques to build a statistical model, using as input a collection of trajectories array through a sensor (e.g., camera, laser scanner), and then using that model to predict further motion.

This section describes the basic concepts involved in current motion learning and prediction approaches. After introducing the Bayes filter, it discusses Growing Hidden Markov Models, an approach which is able to perform lifelong learning, continuously updating its knowledge as more data are available. In experimental evaluation against two other state-of-the-art approaches, the presented approach consistently outperforms them regarding both prediction accuracy and model parsimony.

The section concludes with an overview of the current challenges and future research directions for motion modeling and learning algorithms.


Hide Markov Model Kalman Filter Discrete State Structure Learning Motion Prediction 
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.


  1. Bennewitz M, Burgard W, Cielniak G, Thrun S (2005) Learning motion patterns of people for compliant robot motion. Int J Robot Res 24(1):31–48CrossRefGoogle Scholar
  2. Brand M, Oliver N, Pentland A (1997) Coupled hidden markov models for complex action recognition. In: Proceedings of the 1997 conference on computer vision and pattern recognition, San Juan, pp 994–999Google Scholar
  3. Dee H-M (2005) Explaining visible behaviour. PhD thesis, University of LeedsGoogle Scholar
  4. Hoogs A, Perera AA (2008) Video activity recognition in the real world. In: Proceedings of the twenty-third AAAI conference on artificial intelligence, Chicago, pp 1551–1554Google Scholar
  5. Hu W, Xiao X, Fu Z, Xie D, Tan T, Maybank S (2006) A system for learning statistical motion patterns. IEEE Trans Pattern Anal Mach Intell 28(9):1450–1464CrossRefGoogle Scholar
  6. Jockusch J, Ritter H (1999) An instantaneous topological map for correlated stimuli. In: Proceedings of the international joint conference on neural networks, vol 1, Washington DC, pp 529–534Google Scholar
  7. Juang B-H, Levinson SE, Sondhi MM (1986) Maximum likelihood estimation for multi-variate mixture observations of markov chains. IEEE Trans Inf Theory 32(2):307–309CrossRefGoogle Scholar
  8. Liao L, Fox D, Kautz H (2004) Learning and inferring transportation routines. In: Proceedings of the national conference on artificial intelligence AAAI-04, AmsterdamGoogle Scholar
  9. Magee D (2004) Tracking multiple vehicles using foreground, background and shape models. Image Vision Comput 22:143–155CrossRefGoogle Scholar
  10. Makris D, Ellis T (2002) Spatial and probabilistic modelling of pedestrian behavior. In: Proceedings of the British machine vision conference, Cardiff, pp 557–566Google Scholar
  11. Mozos OM (2008) Semantic place labeling with mobile robots. PhD thesis, University of Freiburg, FreiburgGoogle Scholar
  12. Neal RM, Hinton GE (1998) A new view of the em algorithm that justifies incremental, sparse and other variants. In: Jordan MI (ed) Learning in graphical models. Kluwer Academic, Dordrecht, pp 355–368Google Scholar
  13. Oliver NM, Rosario B, Pentland AE (2000) A Bayesian computer vision system for modeling human interactions. IEEE Trans Pattern Anal Mach Intell 22(8):831–843CrossRefGoogle Scholar
  14. Rabiner LR (1990) A tutorial on hidden markov models and selected applications in speech recognition. Read Speech Recog 77:267–296Google Scholar
  15. Reif J, Sharir M (1985) Motion planning in the presence of moving obstacles. In Symposium on the foundations of computer science, Portland, pp 144–154Google Scholar
  16. Thrun S, Burgard W, Fox D (2005) Probabilistic robotics. MIT Press, Cambridge, MAMATHGoogle Scholar
  17. Vasquez D (2007) Incremental learning for motion prediction of pedestrians and vehicles. PhD thesis, Institut National Polytechnique de Grenoble, Grenoble. Accessed 28 Sept 2011
  18. Vasquez D, Fraichard T, Laugier C (2009) Growing hidden markov models: a tool for incremental learning and prediction of motion. Int J Robot Res 28(11–12):1486–1506CrossRefGoogle Scholar
  19. Walter M, Psarrow A, Gong S (1999) Learning prior and observation augmented density models for behaviour recognition. In: Proceedings of the British machine vision conference, Malvern, Worcestershire, pp 23–32Google Scholar

Copyright information

© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  1. 1.ITESM CuernavacaCuernavacaMexico
  2. 2.e-Motion Project-Team, INRIA Grenoble Rhône-AlpesSaint Ismier, CedexFrance

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