Haptic Learning
Zusammenfassung der Projektergebnisse
In summary, the project has reached most of its major goals: (i) the development of innovative haptic interface devices (iObject+, Twistor) to study manual haptics at high spatial resolution and to support fMRI and behavioral experiments to obtain insights into representations of haptic interaction; (ii) the measurement of haptically driven brain activity in fMRI scans with and without learning in a novel experimental splitting paradigm; (iii) the development of human/robotic behavioral experiment for object recognition and handling; and (iv) the development of a neurodynamic model that can capture some aspects of the processes in a closed haptic sensori-motor loop that establish higher level representations of object features and object-in-hand state from pixel based contact patterns at the finger tips. The cooperation between the German and the Japanese teams linked brain imaging experiments of the Japanese side with the development of a neurodynamic model and robot experiment on the German side. This was strengthened by behavioral experiments conducted on both sides and covering complementary aspects, utilizing the developed haptic interface devices. Besides results in basic research, the technology developments contribute to the development of haptic smart objects with potential applications in the tactile internet.
Projektbezogene Publikationen (Auswahl)
- Correcting pose estimates during tactile exploration of object shape: a neuro-robotic study. ICDL- EPIROB, 2014, 26-33
Strub, C.; Wörgötter, F.; Ritter, H. & Sandamirskaya, Y.
(Siehe online unter https://doi.org/10.1109/DEVLRN.2014.6982950) - Using haptics to extract object shape from rotational manipulations. IROS, 2014, 2179-2186
Strub, C.; Wörgötter, F.; Ritter, H. & Sandamirskaya, Y.
(Siehe online unter https://doi.org/10.1109/IROS.2014.6942856) - (2017) A Neurodynamic Model for Haptic Spatiotemporal Integration. PhD-Dissertation at the Ruhr-University Bochum
Strub, C.
- Dynamic Neural Fields with Intrinsic Plasticity. Frontiers in Computational Neuroscience, 2017
Strub, C.; Schöner, G.; Wörgötter, F. & Sandamirskaya, Y.
(Siehe online unter https://doi.org/10.3389/fncom.2017.00074) - (2018, October). Investigation on the Neural Correlates of Haptic Training. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 519-523). IEEE
Takai, A., Rivela, D., Lisi, G., Noda, T., Teramae, T., Imamizu, H., & Morimoto, J.
(Siehe online unter https://doi.org/10.1109/SMC.2018.00098) - Influence of shape elements on performance during haptic rotation. In International Conference on Human Haptic Sensing and Touch Enabled Computer Applications (pp. 125-137). Springer, Cham. 2018
Krieger, K.; Moringen, A.; Kappers; A. M.; & Ritter H.
(Siehe online unter https://doi.org/10.1007/978-3-319-93445-7_12) - Number of Fingers and Grasping Orientation Influence Human Performance During Haptic Rotation. In 2019 IEEE World Haptics Conference (WHC) (pp. 79-84). IEEE 2019
Krieger, K., Moringen, A., & Ritter, H.
(Siehe online unter https://doi.org/10.1109/WHC.2019.8816096)
