Abstract
Learning from demonstration (LFD) is an active area of research in robotics. There are many approaches to LFD. One of the most widely used approaches is the combination of Gaussian Mixture Model learning for modeling and Gaussian Mixture Regression for behavior generation (GMM/GMR) due to its advantages including easy learning using Expectation Maximization and the simplicity of serializing learned behaviors as well as the ability to model internal correlations and constraints within the task.
A critical parameter that affects the accuracy of learned behavior in GMM/GMR is the number of components in the mixture. A handful of approaches for selecting this number can be found in the literature including classical model selection methods like Bayesian Information Criteria and Akaik Information Criteria and more advanced methods including Dirichlet Process modeling. These approaches are either wasteful of computational resources or hard to implement. This paper introduces a LfD approach which uses GMM with a variational Bayesian Inference (VB) approach to select the number of Gaussians that best fit the data. The proposed method is compared to classical model selection approaches and a recently proposed symbolization based method and is shown to provide an appropriate balance between execution speed and model accuracy.
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References
Ratliff, N., Ziebart, B.D., Peterson, K., Bagnell, J.A., Hebert, M., Dey, A., Srinivasa, S.: Inverse optimal heuristic control for imitation learning. In: Intl. Conf. on Artificial Intelligence and Statistics (AIStats) (2009)
Abbeel, P., Ng, A.Y.: Apprenticeship learning via inverse reinforcement learning. In: Proc. Intl. Conf. on Machine Learning (ICML) (2004)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Mohammad, Y., Nishida, T.: Robust learning from demonstrations using multidimensional SAX. In: ICCAS 2014, Korea (2014)
Mohammad, Y., Nishida, T.: Unsupervised discovery of basic human actions from activity recording datasets. In: IEEE/SICE SII 2012, Kyushu, Japan (2012)
Lopes, M., Melo, F., Montesano, L.: Active learning for reward estimation in inverse reinforcement learning. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part II. LNCS, vol. 5782, pp. 31–46. Springer, Heidelberg (2009)
Ijspeert, A.J., Nakanishi, J., Schaal, S.: Learning attractor landscapes for learning motor primitives. In: Advances in Neural Information Processing Systems, pp. 1523–1530 (2002)
Calinon, S., Guenter, F., Billard, A.: 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). Special issue on robot learning by observation, demonstration and imitation
Bishop, C.M., Corduneanu, A.: Variational bayesian model selection for mixture distributions. In: Jaakkola, T., Richardson, T. (eds.) Artificial Intelligence and Statistics 2001, pp. 27–34 (2001)
Chatzis, S.P., Korkinof, D., Demiris, Y.: A nonparametric bayesian approach toward robot learning by demonstration. Robotics and Autonomous Systems 60(6), 789–802 (2012)
Niekum, S., Osentoski, S., Konidaris, G., Barto, A.G.: Learning and generalization of complex tasks from unstructured demonstrations. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5239–5246 (2012)
Argall, B., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robotics and Autonomous Systems 57(5), 469–483 (2009)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 26(1), 43–49 (1978)
Calinon, S., D’halluin, F., Sauser, E.L., Caldwell, D.G., Billard, A.G.: Learning and reproduction of gestures by imitation: An approach based on Hidden Markov Model and Gaussian Mixture Regression. IEEE Robotics and Automation Magazine 17(2), 44–54 (2010)
Bishop, C.M.: Variational principal components. In: Proceedings Ninth International Conference on Artificial Neural Networks, ICANN 1999, vol. 1, pp. 509–514. IEEE (1999)
Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. In: Jordan, M.I. (ed.) Learning in Graphical Models, pp. 105–162. Kluwer (1998)
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Hussein, M., Mohammed, Y., Ali, S.A. (2015). Learning from Demonstration Using Variational Bayesian Inference. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_36
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DOI: https://doi.org/10.1007/978-3-319-19066-2_36
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