M.Sc. Benedikt Feldotto
Technische Universität München
Informatik 6 - Lehrstuhl für Robotik, Künstliche Intelligenz und Echtzeitsysteme (Prof. Knoll)
85748 Garching b. München
- Tel.: +49 (89) 289 - 17628
Benedikt Feldotto received a Bachelor of Engineering in Mechatronics from Baden-Wuerttemberg Cooperative State University (DHBW), also gaining industry work experience in the pre-development of automation and including a development stay in the USA. Based on his general engineering education he specialized in autonomous robotic systems implementing findings from Cognitive Neuroscience with the Master of Science program in "Robotics, Cognition, Intelligence" at Technical University of Munich (TUM). In 2017 Benedikt Feldotto joined the research group of Neurorobotics in the European Human Brain Project (HBP). His research interests focus on biomimetic learning in neurorobotic systems and the interaction of robots with humans as well as the environment. Additional discussions about ethical concerns arising with learning robots joining our daily life are very welcomed.
|SS 17||Masterpraktikum||Roboy - Simulation and Human Brain Project Integration|
|WS 17/18||Seminar||HBP Neurorobotics|
|SS 18||Lecture Exercise||Cognitive Systems IN2222|
|SS 19||Lecture Exercise|| |
Cognitive Systems IN2222
|SS 20||Lecture Exercise||Cognitive Systems IN2222|
If you are interested in a Thesis, Seminar, Semester Project or HiWi position in biomimetic robots or neural network learning have a look at the current offers or contact me to discuss your personal preferences.
We always have open tasks for Forschungspraktikum e.g. in MSE or Electrical Engineering.
- Co-Development of an Infant Prototype in Hardware and Simulation based on CT Imaging Data. Proceedings IEEE International Conference on Cyborgs and Bionic Systems, 2019 more…
- Motion Prediction of Virtual Patterns, Human Hand Motions and a simplified Hand Manipulation Task with Hierarchical Temporal Memory. Proceedings IEEE International Conference on Cyborgs and Bionic Systems, 2019 more…
- A Control Hierarchy Inpspired by the Spinal Cord to Exploit Self-Organizing Motion Primitives for Purposeful Trajectory Generation. Proceedings of The 28th Annual Conference of the Japanese Neural Network Society, 2018 more…
- Hebbian learning for online prediction, neural recall and classical conditioning of anthropomimetic robot arm motions. Bioinspiration & Biomimetics 13 (6), 2018, 066009 more…