Prof. Dr. Endres, Prof. Dr. Schütz

Perception of Modular Movement Primitives

Project C6 will investigate sensorimotor primitives (SMP): a categorical modeling framework that unifies movement perception, planning and control in a computationally feasible way. We will (machine-)learn Bayesian sensorimotor primitive models from ecologically valid data, and then test these models psychophysically. For movement production, these primitives link the categorical, discrete internal movement representation and continuous motor output. A large number of movement primitive models (MP) have been defined in the literature. We want to determine if these definitions can be extended to sensorimotor primitives, which have measurable perceptual consequences as predicted by Bayesian model comparison.

new project-related publications
A. Serr, M. Schubert, and D. Endres (2018). Mathematical similarity models: do we need incomparability to be precise? In Proceedings of ICCS 2019, pages 88–95, 2019.
Chiovetto, E., Curio, C., Endres, D., & Giese, M. (2018). Perceptual integration of kinematic components in the recognition of emotional facial expressions. Journal of vision, 18(4), 13-13. find paper
Junker, M., Endres, D., Sun, Z. P., Dicke, P. W., Giese, M., & Thier, P. (2018). Learning from the past: A reverberation of past errors in the cerebellar climbing fiber signal. PLoS biology, 16(8), e2004344. find paper
Khoozani, P. A., Schrater, P. R., Endres, D., Fiehler, K., & Blohm, G. (2019). Models of allocentric coding for reaching in naturalistic visual scenes. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, 4 pages.
Knopp, B., Velychko, D., Dreibrodt, J., & Endres, D. (2019). Predicting Perceived Naturalness of Human Animations Based on Generative Movement Primitive Models. ACM Transactions on Applied Perception (TAP), 16(3), 1-18. find paper DOI
Knopp, B., Velychko, D., Dreibrodt, J., Schütz, A. C., & Endres, D. (2020). Evaluating perceptual predictions based on movement primitive models in VR- and online-experiments. In ACM 32 Symposium on Applied Perception 2020, SAP ·20, New York, NY, USA. Association for Computing Machinery. find paper
Schubert, M., & Endres, D. (2018). Empirically Evaluating the Similarity Model of Geist, Lengnink and Wille. In International Conference on Conceptual Structures (pp. 88-95). Springer, Cham. find paper DATA
Serr, A., Schubert, M., & Endres, D. (2019). Mathematical similarity models: Do we need incomparability to be precise? In Graph-Based Representation and Reasoning - 24th International Conference on Conceptual Structures, ICCS 2019, Marburg, Germany, July 1-4, 2019, Proceedings, pages 257-261. find paper
Velychko, D., Knopp, B., & Endres, D (2018). Making the Coupled Gaussian Process Dynamical Model Modular and Scalable with Variational Approximations. Entropy, 20(10), 724. find paper DOI
former project-related publications
Clever, D., Harant, M., Koch, K. H., Mombaur, K. and Endres, D. (2016). A novel approach for the generation of complex humanoid walking sequences based on a combination of optimal control and learning of movement primitives. Robotics and Autonomous Systems, Volume 83, 287–298. find paper
Clever, D., Harant, M., Mombaur, K., Naveau, M., Stasse, O. and Endres, D. (2017). COCoMoPL: A Novel Approach for Humanoid Walking Generation Combining Optimal Control, Movement Primitives and Learning and its transfer to the real robot HRP-2. IEEE Robotics and Automation Letters ,2(2):977 – 984. find paper
Endres , D., Christensen, A., Omlor, L., and Giese, M. A. (2011a). Emulating human observers with Bayesian binning: segmentation of action streams. ACM Transactions on Applied Perception (TAP), 8(3):16:1–12. find paper
Endres , D., Neumann, H., Kolesnik, M., and Giese, M. A. (2011b). Hooligan detection: the effects of saliency and expert knowledge. In Proceedings of the 4th International Conference for Imaging in Crime Detection and Prevention (ICDP 2011), pages 1–6. IET, ISBN-978-1-84919-565-2. find paper
Endres, D., Chiovetto, E. and Giese, M. A. (2013a). Model selection for the extraction of movement primitives. Frontiers in Computational Neuroscience , 7:185. find paper
Endres, D., Meirovitch, Y. Flash, T. and Giese M. A. (2013b). Segmenting sign language into motor primitives with Bayesian binning. Frontiers in Computational Neuroscience , 7:68, 2013. find paper
Mukovskiy, A., Taubert, N., Endres, D., Vassallo, C., Naveau, M., Stasse, O., Souères, P. and Giese, M. A. (2017). Modeling of coordinated human body motion by learning of structured dynamic representations. In J.-P. Laumond, N. Mansard, and J.-B. Lasserre, editors, Geometric and Numerical Foundations of Movements, volume 117 of STAR Series, pages 1–26. Springer. find paper
Quaglio, P., Yegenoglu, A., Torre, E., Endres, D. M., & Grün, S. (2017). Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE. Frontiers in Computational Neuroscience, 11, 41. find paper
Taubert, N., Christensen, A., Endres, D. and Giese, M. A. (2012). Online Simulation of Emotional Interactive Behaviors with Hierarchical Gaussian Process Dynamical Models. Proceedings of the ACM Symposium on Applied Perception (ACM-SAP 2012), pages 25–32. find paper
Taubert, N., Löffler, M., Ludolph, N., Christensen, A., Endres, D. and Giese M.A. (2013). A virtual reality setup for controllable, stylized real-time interactions between humans and avatars with sparse Gaussian process dynamical models. Proceedings of the ACM Symposium on Applied Perception (ACM-SAP 2013), pages 41–44, 2013. find paper
Velychko, D., Endres , D., Taubert, N., and Giese, M. A. (2014). Coupling Gaussian process dynamical models with product-of-experts kernels. In Proceeding of the 24th International Conference on Artificial Neural Networks, LNCS 8681, pages 603–610. Springer. find paper
Velychko, D., Knopp, B. and Endres D. (2017). The coupled variational Gaussian process dynamical model. In Proceedings of the 27th International Conference on Artificial Neural Networks,pages 1–9. DOI find paper
Velychko, D., Knopp, B. and Endres, D. (2016). The variational coupled Gaussian process dynamical model (Abstract). NIPS Workshop on Neurorobotics. DOI find paper