Maria Kyrarini, Germany
Institute of Automation, University of Bremen
WG1, WG2, WG3-member
Maria Kyrarini received her 5-years diploma degree in electrical and computer engineering, in 2012, and the master’s degree in automation systems, in 2014, from National Technical University of Athens. She is currently working as research associate on the project MeRoSy – Human Robot Synergy at the Institute of Automation, University of Bremen, Germany and on the project MobiLe – Physical human-robot interaction for a self-determined life at the FWBI – Friedrich Wilhelm Bessel Institut Forschungsgesellschaft m. b. H., Germany. She is pursuing her Ph.D degree in the topic of machine learning for human robot synergy at University of Bremen under the supervision of Prof. Dr. Axel Gräser. Her research interests are in the area of assistive robotics, wearable sensors and human-robot interaction.
- Kyrarini M., Haseeb M.A., Ristić-Durrant D., Gräser A., 2017. Robot Learning of Object Manipulation Task Actions from Human Demonstrations. Facta Universitatis, series: Mechanical Engineering (FU Mech Eng), 15(2), pp. 217-229. doi: 10.22190/FUME170515010K
- Kyrarini M., Leu A., Ristić-Durrant D., Gräser A., Jackowski A., Gebhard M., Nelles J., Bröhl C., Brandl C., Mertens A., Schlick C. M., 2016. Human-Robot Synergy for cooperative robots. Facta Universitatis, series: Autonomic Control and Robotics, 15(3), pp. 187-204. doi: 10.22190/FUACR1603187K
- Kyrarini M., Gräser A., 2017. Selection of Human Demonstrations for Robot Learning of Industrial Scenario. In 2017 IEEE/RSJ IROS WORKSHOP Human in-the-loop robotic manipulation: on the influence of the human role (2-pages extended abstract)
- Kyrarini M., Naeem S., Wang X., Gräser A., 2017. Skill Robot Library: Intelligent Path Planning Framework for Object Manipulation. In 25th European Signal Processing Conference (EUSIPCO 2017), Kos island, pp.2398 – 2402. IEEE, doi: 10.23919/EUSIPCO.2017.8081640
- Wang X., Kyrarini M., Ristić-Durrant D., Spranger M., Gräser A., 2016. Monitoring of Gait Performance Using Dynamic Time Warping on IMU-Sensor Data. In 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1-6. IEEE. doi: 10.1109/MeMeA.2016.7533745
