Abstract
We propose a model for learning the articulated motion of human arm. The goal is to generate plausible trajectories of joints that mimic the human movement using deformation information. The trajectories are then mapped to constraint space. These constraints can be the space of start and end configuration of the human body and task-specific constraints such as avoiding an obstacle, picking up and putting down objects. This movement generalization is a step forward from existing systems that can learn single gestures only. Such a model can be used to develop humanoid robots that move in a human-like way in reaction to diverse changes in their environment. The model proposed to accomplish this uses a combination of principal component analysis (PCA) and a special type of a topological map called the dynamic cell structure (DCS) network. Experiments on a kinematic chain of 2 joints show that this model is able to successfully generalize movement using a few training samples for both free movement and obstacle avoidance.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Urtasun, R., Fleet, D.J., Hertzmann, A., Fua, P.: Priors for people tracking from small training sets. In: International Conference on Computer Vision (ICCV), pp. 403–410 (2005)
Grochow, K., Martin, S.L., Hertzmann, A., Popovic, Z.: Style-based inverse kinematics. ACM Trans. Graph. 23(3), 522–531 (2004)
Schaal, S., Peters, J., Nakanishi, J., Ijspeert, A.: Learning movement primitives. In: International Symposium on Robotics Research (ISPR 2003), Springer Tracts in Advanced Robotics, Ciena, Italy (2004)
Sidenbladh, H., Black, M.J., Fleet, D.J.: Stochastic tracking of 3D human figures using 2D image motion. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 702–718. Springer, Heidelberg (2000)
Sminchisescu, C., Jepson, A.: Generative modeling for continuous non-linearly embedded visual inference. In: Proceedings of the twenty-first International Conference on Machine Learning (ICML 2004). ACM Press, New York (2004)
Ilg, W., Bakir, G.H., Mezger, J., Giese, M.A.: On the repersenation, learning and transfer of spatio-temporal movement characteristics. International Journal of Humanoid Robotics (2004)
Gleicher, M.: Retargeting motion to new characters. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH 1998), pp. 33–42. ACM Press, New York (1998)
Banarer, V.: Struktureller bias in neuronalen netzen mittels clifford-algebren. Technical Report 0501, Technische Fakultät der Christian-Albrechts-Universität zu Kiel, Kiel (2005)
Bruske, J., Sommer, G.: Dynamic cell structure learns perfectly topology preserving map. Neural Computation 7(4), 845–865 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Al-Zubi, S., Sommer, G. (2006). Learning Deformations of Human Arm Movement to Adapt to Environmental Constraints. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2006. Lecture Notes in Computer Science, vol 4069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11789239_21
Download citation
DOI: https://doi.org/10.1007/11789239_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36031-5
Online ISBN: 978-3-540-36032-2
eBook Packages: Computer ScienceComputer Science (R0)