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Visual Analysis of Humans pp 333–353Cite as

Learning Action Primitives

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Abstract

The use of action primitives plays an important role in modeling actions. Action primitives are motivated not only by neurobiological findings, they also allow an efficient and effective action modeling from an information-theoretic viewpoint. Different approaches for modeling action primitives have been proposed. This chapter overviews the recent approaches for learning and modeling action primitives for human and robot action and describes common approaches such as stochastic methods and dynamical systems approaches. Active research questions in the field are introduced, including temporal segmentation, dimensionality reduction, and the integration of action primitives into complex behaviors.

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References

  1. Aleotti, J., Caselli, S., Reggiani, M.: Leveraging on a virtual environment for robot programming by demonstration. Robot. Auton. Syst. 47(2–3), 153–161 (2004)

    Google Scholar 

  2. Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv. Neural Inf. Process. Syst. 14, 585–591 (2002)

    Google Scholar 

  3. Bennewitz, M., Burgard, W., Cielniak, G., Thrun, S.: Learning motion patterns of people for compliant robot motion. Int. J. Robot. Res. 24(1), 31–48 (2005)

    Google Scholar 

  4. Bernardin, K., Ogawara, K., Ikeuchi, K., Dillmann, R.: A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models. IEEE Trans. Robot. 21(1), 47–57 (2005)

    Google Scholar 

  5. Billard, A., Calinon, S., Guenter, F.: Discriminative and adaptive imitation in uni-manual and bi-manual tasks. Robot. Auton. Syst. 54, 370–384 (2006)

    Google Scholar 

  6. Billard, A., Calinon, S., Dillmann, R., Schaal, S.: Robot programming by demonstration. In: Siciliano, B., Khatib, O. (eds.) Handbook of Robotics, pp. 1371–1394. Springer, Berlin (2008)

    Google Scholar 

  7. Bitzer, S., Vijayakumar, S.: Latent spaces for dynamic movement primitives. In: IEEE Int. Conf. on Humanoid Robots, pp. 574–581 (2009)

    Google Scholar 

  8. Breazeal, C., Scassellati, B.: Robots that imitate humans. Trends Cogn. Sci. 6(11), 481–487 (2002)

    Google Scholar 

  9. Byrne, R.W., Russon, A.E.: Learning by imitation: A hierarchical approach. Behav. Brain Sci. 21, 667–721 (1998)

    Google Scholar 

  10. Calinon, S., Billard, A.: Active teaching in robot programming by demonstration. In: IEEE Int. Conf. on Robot and Human Interactive Communication, pp. 702–707 (2007)

    Google Scholar 

  11. Calinon, S., Billard, A.: Incremental learning of gestures by imitation in a humanoid robot. In: ACM/IEEE Int. Conf. on Human–Robot Interaction, pp. 255–262 (2007)

    Google Scholar 

  12. Calinon, S., Billard, A.: Learning of gestures by imitation in a humanoid robot. In: Nehaniv, C.L., Dautenhahn, K. (eds.) Imitation and Social Learning in Robots, Humans and Animals, pp. 153–177. Cambridge University Press, Cambridge (2007)

    Google Scholar 

  13. Calinon, S., D’halluin, F., Sauser, E.L., Caldwell, D.G., Billard, A.G.: Learning and reproduction of gestures by imitation. IEEE Robot. Autom. Mag. 17(2), 44–54 (2010)

    Google Scholar 

  14. Calinon, S., Guenter, F., Billard, A.: On learning, representing and generalizing a task in a humanoid robot. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 37(2), 286–298 (2007)

    Google Scholar 

  15. Cox, T.F., Cox, M.A.A.: Multidimensional Scaling. Chapman & Hall, London (2001)

    MATH  Google Scholar 

  16. Tung, C.P., Kak, A.C.: Automatic learning of assembly task using DataGlove system. In: IEEE Int. Conf. on Intelligent Robots and Systems, vol. 1, pp. 1–8 (1995)

    Google Scholar 

  17. Demiris, Y., Johnson, M.: Distributed, predictive perception of actions: A biologically inspired robotics architecture for imitation and learning. Connect. Sci. 15(4), 231–243 (2003)

    Google Scholar 

  18. Dillmann, R.: Teaching and learning of robot tasks via observation of human performance. Robot. Auton. Syst. 47, 109–116 (2004)

    Google Scholar 

  19. Dillmann, R., Rogalla, O., Ehrenmann, M., Zollner, R., Bordegoni, M.: Learning robot behaviour and skills based on human demonstration and advice: The machine learning paradigm. In: Int. Symp. on Robotics Research, pp. 229–238 (1999)

    Google Scholar 

  20. Dixon, K.R., Dolan, J.M., Khosla, P.K.: Predictive robot programming: Theoretical and experimental analysis. Int. J. Robot. Res. 23(9), 955–973 (2004)

    Google Scholar 

  21. Dogar, M.R., Cakmak, M., Ugur, E., Sahin, E.: From primitive behaviors to goal-directed behavior using affordances. In: IEEE Int. Conf. on Intelligent Robots and Systems, pp. 729–734 (2007)

    Google Scholar 

  22. Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge University Press, Cambridge (1998)

    MATH  Google Scholar 

  23. Ekvall, S., Aarno, D., Kragic, D.: Online task recognition and real-time adaptive assistance for computer-aided machine control. IEEE Trans. Robot. 22(5), 1029–1033 (2006)

    Google Scholar 

  24. Ekvall, S., Kragic, D.: Interactive grasp learning based on human demonstration. In: IEEE Int. Conf. on Robotics and Automation, vol. 4, pp. 3519–3524 (2004)

    Google Scholar 

  25. Ekvall, S., Kragic, D.: Grasp recognition for programming by demonstration tasks. In: IEEE Int. Conf. on Robotics and Automation, pp. 748–753 (2005)

    Google Scholar 

  26. Fitzpatrick, P., Metta, G., Natale, L., Rao, S., Sandini, G.: Learning about objects through action - initial steps towards artificial cognition. In: IEEE Int. Conf. on Robotics and Automation, vol. 3, pp. 3140–3145 (2003)

    Google Scholar 

  27. Fod, A., Matarić, M.J., Jenkins, O.C.: Automated derivation of primitives for movement classification. Auton. Robots 12(1), 39–54 (2002)

    MATH  Google Scholar 

  28. Gibson, J.J.: The theory of affordances. In: Shaw, R., Bransford, J. (eds.) Perceiving, Acting and Knowing: Toward an Ecological Psychology, pp. 67–82. Lawrence Erlbaum Associates Publishers, New York (1977)

    Google Scholar 

  29. Gribovskaya, E., Khansari-Zadeh, S.M., Billard, A.: Learning non-linear multivariate dynamics of motion in robotic manipulators. Int. J. Robot. Res. (2010, in press)

    Google Scholar 

  30. Griffith, S., Sinapov, J., Miller, M., Stoytchev, A.: Toward interactive learning of object categories by a robot: A case study with container and non-container objects. In: IEEE Int. Conf. on Development and Learning, pp. 1–6 (2009)

    Google Scholar 

  31. Guenter, F., Billard, A.G.: Using reinforcement learning to adapt an imitation task. In: IEEE Int. Conf. on Intelligent Robots and Systems, pp. 1022–1027 (2007)

    Google Scholar 

  32. Han, L., Wu, X., Liang, W., Hou, G., Jia, Y.: Discriminative human action recognition in the learned hierarchical manifold space. Image Vis. Comput. 28, 836–849 (2010)

    Google Scholar 

  33. Heyes, C.: Causes and consequences of imitation. Trends Cogn. Sci. 5(6), 253–261 (2001)

    Google Scholar 

  34. Heyes, C., Ray, E.: What is the significance of imitation in animals? Adv. Study Behav. 29, 215–245 (2000)

    Google Scholar 

  35. Ho, M.A.T., Yamada, Y., Umetani, Y.: An adaptive visual attentive tracker for human communicational behaviors using HMM-based TD learning with new state distinction capability. IEEE Trans. Robot. 21(3), 497–504 (2005)

    Google Scholar 

  36. Iba, S., Paredis, C.J.J., Khosla, P.K.: Interactive multi-modal robot programming. Int. J. Robot. Res. 24(1), 83–104 (2005)

    Google Scholar 

  37. Ijspeert, A.J., Nakanishi, J., Schaal, S.: Movement imitation with nonlinear dynamical systems in humanoid robots. In: IEEE Int. Conf. on Robotics and Automation, pp. 1398–1403 (2002)

    Google Scholar 

  38. Ikeuchi, K., Suchiro, T.: Towards an assembly plan from observation, part i: Assembly task recognition using face-contact relations (polyhedral objects). In: IEEE Int. Conf. on Robotics and Automation, vol. 3, pp. 2171–2177 (1992)

    Google Scholar 

  39. Inamura, T., Toshima, I., Tanie, H., Nakamura, Y.: Embodied symbol emergence based on mimesis theory. Int. J. Robot. Res. 23(4–5), 363–377 (2004)

    Google Scholar 

  40. Ito, M., Tani, J.: On-line imitative interaction with a humanoid robot using a dynamic neural network model of a mirror system. Adapt. Behav. 12(2), 93–115 (2004)

    Google Scholar 

  41. Jacob, R., Richards, C., Hannaford, B., Sinanan, M.N.: Hidden Markov models of minimally invasive surgery. Stud. Health Technol. Inform. 70, 279–285 (2000)

    Google Scholar 

  42. Janus, B., Nakamura, Y.: Unsupervised probabilistic segmentation of motion data for mimesis modeling. In: IEEE Int. Conf. on Advanced Robotics, pp. 411–417 (2005)

    Google Scholar 

  43. Jenkins, O.C., Matarić, M.: Performance-derived behavior vocabularies: Data-driven acquisition of skills from motion. Int. J. Humanoid Robot. 1(2), 237–288 (2004)

    Google Scholar 

  44. Jenkins, O.C., Matarić, M.: A spatio-temporal extension to isomap nonlinear dimension reduction. In: Int. Conf. on Machine Learning, pp. 441–448 (2004)

    Google Scholar 

  45. Jolliffe, I.T.: Principal Component Analysis. Springer, New York (2002)

    MATH  Google Scholar 

  46. Kang, S.B., Ikeuchi, K.: Toward automatic robot instruction from perception – temporal segmentation of tasks from human hand motion. IEEE Trans. Robot. Autom. 11, 432–443 (1993)

    Google Scholar 

  47. Kjellstrom, H., Romero, J., Kragic, D.: Visual object-action recognition: Inferring object affordances from human demonstration. Comput. Vis. Image Underst. 115, 81–90 (2011)

    Google Scholar 

  48. Kober, J., Mohler, B., Peters, J.: Learning perceptual coupling for motor primitives. In: IEEE Int. Conf. on Intelligent Robots and Systems (2008)

    Google Scholar 

  49. Koenig, N., Matarić, M.J.: Behavior-based segmentation of demonstrated tasks. In: Int. Conf. on Development and Learning (2006)

    Google Scholar 

  50. Kohlmorgen, J., Lemm, S.: A dynamic HMM for on-line segmentation of sequential data. In: Neural Information Processing Systems, pp. 793–800 (2001)

    Google Scholar 

  51. Kozima, H., Nakagawa, C., Yano, H.: Emergence of imitation mediated by objects. In: Int. Workshop on Epigenetic Robotics, pp. 59–61 (2002)

    Google Scholar 

  52. Kragic, D., Marayong, P., Li, M., Okamura, A.M., Hager, G.D.: Human–machine collaborative systems for microsurgical applications. Int. J. Robot. Res. 24(9), 731–742 (2005)

    Google Scholar 

  53. Krueger, V., Grest, D.: Using hidden Markov models for recognizing action primitives in complex actions. In: Scandinavian Conf. on Image Analysis, pp. 203–212 (2007)

    Google Scholar 

  54. Krüger, V., Herzog, D., Baby, S., Ude, A., Kragic, D.: Learning actions from observations. IEEE Robot. Autom. Mag. 17(2), 30–43 (2010).

    Google Scholar 

  55. Kulić, D., Nakamura, Y.: On-line segmentation of whole body human motion data for large kinematic models. In: IEEE Int. Conf. on Intelligent Robots and Systems, pp. 4300–4305 (2009)

    Google Scholar 

  56. Kulić, D., Takano, W., Nakamura, Y.: Incremental on-line hierarchical clustering of whole body motion patterns. In: IEEE Int. Symp. on Robot and Human Interactive Communication, pp. 1016–1021 (2007)

    Google Scholar 

  57. Kulić, D., Takano, W., Nakamura, Y.: Incremental learning, clustering and hierarchy formation of whole body motion patterns using adaptive hidden Markov chains. Int. J. Robot. Res. 27(7), 761–784 (2008)

    Google Scholar 

  58. Kulić, D., Takano, W., Nakamura, Y.: On-line segmentation and clustering from continuous observation of whole body motions. IEEE Trans. Robot. 25(5), 1158–1166 (2009)

    Google Scholar 

  59. Kuniyoshi, Y., Inaba, M., Inoue, H.: Teaching by showing: Generating robot programs by visual observation of human performance. In: Int. Symp. on Industrial Robots, pp. 119–126 (1989)

    Google Scholar 

  60. Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Int. Conf. on Machine Learning, pp. 282–289 (2001)

    Google Scholar 

  61. Lawrence, N.D.: Probabilistic non-linear principal component analysis with Gaussian process latent variable models. J. Mach. Learn. Res. 6, 1783–1816 (2005)

    MathSciNet  Google Scholar 

  62. Lieberman, J., Breazeal, C.: Improvements on action parsing and action interpolation for learning through demonstration. In: IEEE Int. Conf. on Humanoid Robots, pp. 342–365 (2004)

    Google Scholar 

  63. Loesch, M., Schmidt-Rohr, S., Knoop, S., Vacek, S., Dillmann, R.: Feature set selection and optimal classifier for human activity recognition. In: IEEE Int. Conf. on Robot and Human Interactive Communication, pp. 1022–1027 (2007)

    Google Scholar 

  64. Lopes, M.C., Victor, J.S.: Visual learning by imitation with motor representations. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 35(3), 438–449 (2005)

    Google Scholar 

  65. Manning, C., Schuetze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  66. Meltzoff, A.N.: Imitation as a mechanism of social cognition: Origins of empathy, theory of mind, and the representation of action. In: Goswami, U. (ed.) Blackwell Handbook of Childhood Cognitive Development, pp. 6–25. Blackwell Sci., Oxford (2002)

    Google Scholar 

  67. Meltzoff, A.N.: Imitation and other minds: The ‘like me’ hypothesis. In: Hurley, S., Chater, N. (eds.) Perspectives on Imitation: From Neuroscience to Social Science, vol. 2, pp. 55–77. MIT Press, Cambridge (2005)

    Google Scholar 

  68. Metta, G., Sandini, G., Natale, L., Manzotti, R., Panerai, F.: Development in artificial systems. In: EDEC Symp. at the Int. Conf. on Cognitive Science, Beijing, China (2001)

    Google Scholar 

  69. Montesano, L., Lopes, M., Bernardino, A., Santos-Victor, J.: Learning object affordances: From sensory motor coordination to imitation. IEEE Trans. Robot. 24(1), 15–26 (2008)

    Google Scholar 

  70. Mussa-Ivaldi, F.A., Bizzi, E.: Motor learning through the combination of primitives. Philos. Trans. R. Soc. Lond. B, Biol. Sci. 355, 1755–1769 (2000)

    Google Scholar 

  71. Nakanishi, J., Morimoto, J., Endo, G., Cheng, G., Schaal, S., Kawato, M.: Learning from demonstration and adaptation of biped locomotion. Robot. Auton. Syst. 47, 79–91 (2004)

    Google Scholar 

  72. Ogata, T., Sugano, S., Tani, J.: Open-end human–robot interaction from the dynamical systems perspective: mutual adaptation and incremental learning. Adv. Robot. 19, 651–670 (2005)

    Google Scholar 

  73. Okada, M., Tatani, K., Nakamura, Y.: Polynomial design of the nonlinear dynamics for the brain-like information processing of whole body motion. In: IEEE Int. Conf. on Robotics and Automation, pp. 1410–1415 (2002)

    Google Scholar 

  74. Paradowitz, M., Zoellner, R., Knoop, S., Dillmann, R.: Incremental learning of tasks from user demonstrations, pas experiences and vocal comments. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 37(2), 322–332 (2007)

    Google Scholar 

  75. Pastor, P., Hoffmann, H., Asfour, T., Schaal, S.: Learning and generalization of motor skills by learning from demonstration. In: IEEE Int. Conf. on Robotics and Automation, pp. 763–768 (2009)

    Google Scholar 

  76. Peters, J., Schaal, S.: Applying the episodic natural actor-critic architecture to motor primitive learning. In: European Symposium on Artificial Neural Networks (2007)

    Google Scholar 

  77. Peters, J., Schaal, S.: Reinforcement learning for operational space control. In: IEEE Int. Conf. on Robotics and Automation, pp. 2111–2116 (2007)

    Google Scholar 

  78. Pomplun, M., Matarić, M.J.: Evaluation metrics and results of human arm movement imitation. In: IEEE Int. Conf. on Humanoid Robotics (2000)

    Google Scholar 

  79. Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)

    Google Scholar 

  80. Redner, R.A., Walker, H.F.: Mixture densities, maximum likelihood and the em algorithm. SIAM Rev. 26(2), 195–239 (1984)

    MathSciNet  MATH  Google Scholar 

  81. Reiley, C.E., Plaku, E., Hager, G.D.: Motion generation of robotic surgical tasks: Learning from expert demonstrations. In: Int. Conf. of the IEEE Engineering in Medicine and Biology Society (2010)

    Google Scholar 

  82. Rizzolatti, G., Craighero, L.: The mirror-neuron system. Annu. Rev. Neurosci. 27, 169–192 (2004)

    Google Scholar 

  83. Rizzolatti, G., Fogassi, L., Gallese, V.: Neurophysical mechanisms underlying the understanding and imitation of action. Nat. Rev., Neurosci. 2, 661–670 (2001)

    Google Scholar 

  84. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)

    Google Scholar 

  85. Sahin, E., Cakmak, M., Dogar, M.R., Ugur, E., Uecoluk, G.: To afford or not to afford: A new formalization of affordances toward affordance-based robot control. Adapt. Behav. 15(4), 447–472 (2007)

    Google Scholar 

  86. Schaal, S.: Dynamic movement primitives – a framework for motor control in humans and humanoid robotics. In: Kimura, H., Tsuchiya, K., Ishiguro, A., Witte, H. (eds.) Adaptive Motion of Animals and Machines, pp. 261–280. Springer, Tokyo (2006)

    Google Scholar 

  87. Schaal, S., Atkeson, C.G., Vijayakumar, S.: Scalable techniques from nonparametric statistics for real time robot learning. Appl. Intell. 17, 49–60 (2002)

    MATH  Google Scholar 

  88. Schaal, S., Ijspeert, A., Billard, A.: Computational approaches to motor learning by imitation. Philos. Trans. R. Soc. Lond. B, Biol. Sci. 358, 537–547 (2003)

    Google Scholar 

  89. Shi, Q., Wang, L., Cheng, L., Smola, A.: Human action segmentation and recognition using discriminative semi-Markov models. Int. J. Comput. Vis. 1–11 (2010)

    Google Scholar 

  90. Sinapov, J., Stoytchev, A.: Detecting the functional similarities between tools using a hierarchical representation of outcomes. In: IEEE Int. Conf. on Development and Learning, pp. 91–96 (2008)

    Google Scholar 

  91. Song, D., Huebner, K., Kyrki, V., Kragic, D.: Learning task constraints for robot grasping using graphical models. In: IEEE Int. Conf. on Intelligent Robots and Systems, pp. 1579–1585 (2010)

    Google Scholar 

  92. Startner, T., Pentland, A.: Visual recognition of American sign language using hidden Markov models. In: Int. Conf. on Automatic Face and Gesture Recognition, pp. 189–194 (1995)

    Google Scholar 

  93. Sugiura, K., Iwahashi, N.: Learning object-manipulation verbs for human–robot communication. In: Workshop on Multi-Modal Interfaces in Semantic Interaction, pp. 32–38 (2007)

    Google Scholar 

  94. Sugiura, K., Iwahashi, N.: Motion recognition and generation by combining reference-point-dependent probabilistic models. In: IEEE Int. Conf. on Intelligent Robots and Systems, pp. 852–857 (2008)

    Google Scholar 

  95. Takano, W., Yamane, K., Sugihara, T., Yamamoto, K., Nakamura, Y.: Primitive communication based on motion recognition and generation with hierarchical mimesis model. In: IEEE Int. Conf. on Robotics and Automation, pp. 3602–3608 (2006)

    Google Scholar 

  96. Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)

    Google Scholar 

  97. Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. J. R. Stat. Soc., Ser. B, Stat. Methodol. 61(3), 611–622 (1999)

    MathSciNet  MATH  Google Scholar 

  98. Ude, A., Atkeson, C.G., Riley, M.: Programming full-body movements for humanoid robots by observation. Robot. Auton. Syst. 47(2–3), 93–108 (2004)

    Google Scholar 

  99. Ude, A., Riley, M., Nemec, B., Kos, A., Asfour, T., Cheng, G.: Synthesizing goal-directed actions from a library of example movements. In: IEEE Int. Conf. on Humanoid Robots, pp. 115–121 (2007)

    Google Scholar 

  100. Ude, A., Gams, A., Asfour, T., Morimoto, J.: Task-specific generalization of discrete and periodic dynamic movement primitives. IEEE Trans. Robot. 26(5), 800–815 (2010)

    Google Scholar 

  101. Vijayakumar, S., D’Souza, A., Schaal, S.: Incremental online learning in high dimensions. Neural Comput. 17, 2602–2634 (2005)

    MathSciNet  Google Scholar 

  102. Weinberger, K.Q., Sha, F., Saul, L.K.: Learning a kernel matrix for nonlinear dimensionality reduction. In: Int. Conf. Machine Learning, pp. 106–111 (2004)

    Google Scholar 

  103. Wilson, A.D., Bobick, A.F.: Parametric hidden Markov models for gesture recognition. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 884–900 (1999)

    Google Scholar 

  104. Wohlschlaeger, A., Gattis, M., Bekkering, H.: Action generation and action perception in imitation: An instance of the ideomotor principle. Philos. Trans. R. Soc. Lond. B, Biol. Sci. 358, 501–515 (2003)

    Google Scholar 

  105. Yang, J., Xu, Y., Chen, C.S.: Human action learning via hidden Markov model. IEEE Trans. Syst. Man Cybern., Part A, Syst. Hum. 27(1), 34–44 (1997)

    Google Scholar 

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Authors and Affiliations

  1. University of Waterloo, Waterloo, Canada

    Dana Kulić

  2. Centre for Autonomous Systems, Royal Institute of Technology – KTH, Stockholm, Sweden

    Danica Kragic

  3. Aalborg University Copenhagen, Ballerup, Denmark

    Volker Krüger

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  1. Dana Kulić
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  2. Danica Kragic
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  3. Volker Krüger
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Correspondence to Dana Kulić .

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Editors and Affiliations

  1. Department of Media Technology, Aalborg University, Niels Jernes Vej 14, Aalborg, 9220, Denmark

    Thomas B. Moeslund

  2. Centre for Vision, Speech & Signal Proc., University of Surrey, Guildford, GU2 7XH, Surrey, United Kingdom

    Adrian Hilton

  3. Copenhagen Institute of Technology, Aalborg University, Lautrupvang 2B, Ballerup, 2750, Denmark

    Volker Krüger

  4. Disney Research, Forbes Avenue 615, Pittsburgh, 15213, Pennsylvania, USA

    Leonid Sigal

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Kulić, D., Kragic, D., Krüger, V. (2011). Learning Action Primitives. In: Moeslund, T., Hilton, A., Krüger, V., Sigal, L. (eds) Visual Analysis of Humans. Springer, London. https://doi.org/10.1007/978-0-85729-997-0_17

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