Abstract
We propose a novel methodology for learning and synthesising whole classes of high dimensional movements from a limited set of demonstrated examples that satisfy some underlying ’latent’ low dimensional task constraints. We employ non-linear dimensionality reduction to extract a canonical latent space that captures some of the essential topology of the unobserved task space. In this latent space, we identify suitable parametrisation of movements with control policies such that they are easily modulated to generate novel movements from the same class and are robust to perturbations. We evaluate our method on controlled simulation experiments with simple robots (reaching and periodic movement tasks) as well as on a data set of very high-dimensional human (punching) movements. We verify that we can generate a continuum of new movements from the demonstrated class from only a few examples in both robotic and human data.
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References
Schaal, S., Ijspeert, A., Billard, A.: Computational approaches to motor learning by imitation. Phil. Trans. Royal Soc. London B Biol. Sci. 358(1431), 537–547 (2003)
Grimes, D.B., Chalodhorn, R., Rao, R.P.N.: Dynamic imitation in a humanoid robot through nonparametric probabilistic inference. In: Proc. Robotics: Science & Systems (2006)
Peters, J., Schaal, S.: Reinforcement learning for parameterized motor primitives. In: 2006 International Joint Conference on Neural Networks, IJCNN, pp. 73–80 (2006)
Wiley, D.J., Hahn, J.K.: Interpolation synthesis of articulated figure motion. IEEE Computer Graphics and Applications 17(6), 39–45 (1997)
Giese, M.A., Poggio, T.: Morphable models for the analysis and synthesis of complex motion patterns. International Journal of Computer Vision 38(1), 59–73 (2000)
Grochow, K., Martin, S.L., Hertzmann, A., Popovic, Z.: Style-based inverse kinematics. In: ACM Transactions on Graphics (Proceedings of SIGGRAPH) (2004)
Tatani, K., Nakamura, Y.: Dimensionality reduction and reproduction with hierarchical NLPCA neural networks - extracting common space of multiple humanoid motion patterns. In: Proc. IEEE Intl. Conf. on Robotics and Automation, pp. 1927–1932. ICRA (2003)
Wang, J.M., Fleet, D.J., Hertzmann, A.: Gaussian process dynamical models for human motion. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(2), 283–298 (2008)
Carreira-Perpinan, M.A., Lu, Z.: The laplacian eigenmaps latent variable model. In: Proc. of the 11th Intl. Conference on Artificial Intelligence and Statistics. AISTATS (2007)
Lawrence, N.: Probabilistic non-linear principal component analysis with gaussian process latent variable models. Journal of Machine Learning Research 6, 1783–1816 (2005)
Ijspeert, A.J., Nakanishi, J., Schaal, S.: Learning attractor landscapes for learning motor primitives. In: Advances in Neural Information Processing Systems, vol. 15, pp. 1523–1530 (2003)
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© 2008 Springer-Verlag Berlin Heidelberg
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Bitzer, S., Havoutis, I., Vijayakumar, S. (2008). Synthesising Novel Movements through Latent Space Modulation of Scalable Control Policies. In: Asada, M., Hallam, J.C.T., Meyer, JA., Tani, J. (eds) From Animals to Animats 10. SAB 2008. Lecture Notes in Computer Science(), vol 5040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69134-1_20
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DOI: https://doi.org/10.1007/978-3-540-69134-1_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69133-4
Online ISBN: 978-3-540-69134-1
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