Hierarchical Bayesian Modeling of Manipulation Sequences from Bimodal Input

Barchunova A, Moringen J, Haschke R, Ritter H. Hierarchical Bayesian Modeling of Manipulation Sequences from Bimodal Input. Presented at the Proceedings of the 11th International Conference on Cognitive Modeling, Berlin

Manual interaction: multimodality, decomposition, recognition

Barchunova A. Manual interaction: multimodality, decomposition, recognition. Bielefeld: Bielefeld University; 2013

Identification of High-level Object Manipulation Operations from Multimodal Input

Barchunova A, Franzius M, Pardowitz M, Ritter H. Identification of High-level Object Manipulation Operations from Multimodal Input. Presented at the IASTED International Conferences on Automation, Control, and Information Technology

Multimodal Segmentation of Object Manipulation Sequences with Product Models

Barchunova A, Haschke R, Franzius M, Ritter H. Multimodal Segmentation of Object Manipulation Sequences with Product Models. Presented at the International Conference on Multimodal Interaction, Alicante

Unsupervised Segmentation of Object Manipulation Operations from Multimodal Input

Barchunova A, Moringen J, Großekathöfer U, et al. Unsupervised Segmentation of Object Manipulation Operations from Multimodal Input. In: Hammer B, Villmann T, eds. Machine Learning Reports. New Challenges in Neural Computation. 2011: 9

Learning of Object Manipulation Operations from Continuous Multimodal Input

Großekathöfer U, Barchunova A, Haschke R, Hermann T, Franzius M, Ritter H. Learning of Object Manipulation Operations from Continuous Multimodal Input. In: IEEE/RAS International Conference on Humanoid Robots 2011. 2011.In this paper we propose an approach for identification of high-level object manipulation operations within a continuous multimodal time-series. We focus on a multimodal approach for robust recognition of action primitive data. Our procedure combines an unsupervised Bayesian multimodal segmentation with a supervised machine learning approach. We briefly outline (1) the unsupervised segmentation and selection of uni- and bi-manual manipulation primitives developed in our previous work. We show (2) an application of the ordered means models to classification of estimated segments. To assess the performance of our approach, we compare the computed labels to the ground truth acquired during the data recording. In our experiments we examined the robustness of the procedure on two different sets of segments: full length (≈ 95% overlap with the ground truth on average), partial length (≈ 10% overlap with ground truth on average). We have achieved a cross validation rate of ≈ 0.95 and recognition accuracy of ≈ 0.97 for full length and ≈ 0.84 for partial length test sets

Unsupervised Identification of Object Manipulation Operations from Multimodal Input

Barchunova A, Moringen J, Grossekathoefer U, et al. Unsupervised Identification of Object Manipulation Operations from Multimodal Input. In: Hammer B, Villmann T, eds. Workshop New Challenges in Neural Computation 2011. Machine Learning Reports. Vol 2011. 2011: 42-50