Ph.D. Student at the University of Luebeck
Short bio: Dr. Daniel Tanneberg passed his PhD defense on the 3rd of December in 2020. He is now working as senior researcher at the Honda Research Institute in Offenbach, Germany.
He was co-supervised by Prof. Jan Peters from the Technische Universitaet Darmstadt and Univ.-Prof. Dr. Elmar Rueckert, the head of this lab.
Daniel has joined the Intelligent Autonomous Systems (IAS) Group at the Technische Universitaet Darmstadt in October 2015 as a Ph.D. Student. His research focused on (biologically-inspired) machine learning for robotics and neuroscience. During his Ph.D., Daniel investigated the applicability and properties of spiking and memory-augmented deep neural networks. His neural networks were applied to robotic as well as to algorithmic tasks.
With his masters thesis with the title Neural Networks Solve Robot Planning Problems he won the prestigoues Hanns-Voith-Stiftungspreis 2017 ’Digital Solutions’.
- (Biologically-inspired) Machine Learning, (Memory-augmented) Neural Networks, Deep Learning, (Stochastic) Neural Networks, Lifelong-Learning.
Contact & Quick Links
SKID RAW: Skill Discovery from Raw Trajectories Journal Article
In: IEEE Robotics and Automation Letters (RA-L), pp. 1–8, 2021, ISSN: 2377-3766, (© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.).
In: Nature Machine Intelligence, pp. 1–11, 2020.
In: Neural Networks – Elsevier, vol. 109, pp. 67-80, 2019, ISBN: 0893-6080, (Impact Factor of 7.197 (2017)).
In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017.
In: Workshop at the International Conference on Humanoid Robots (HUMANOIDS), 2017.
In: Proceedings of the Conference on Robot Learning (CoRL), 2017.
In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2016.
Recurrent Spiking Networks Solve Planning Tasks Journal Article
In: Nature Publishing Group: Scientific Reports, vol. 6, no. 21142, 2016.
Adaptive Training Strategies for BCIs Inproceedings
In: Cybathlon Symposium, 2016.
Spiking Neural Networks Solve Robot Planning Problems Technical Report
Technische Universität Darmstadt M.Sc. Thesis, 2015.