Dr. Daniel Tanneberg

Ph.D. Student at the University of Luebeck

Portrait of Daniel Tanneberg, Jan. 2018

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’.

Research Interests

  • (Biologically-inspired) Machine Learning, (Memory-augmented) Neural Networks, Deep Learning, (Stochastic) Neural Networks, Lifelong-Learning.

Contact & Quick Links

Dr. Daniel Tanneberg
Former Doctoral Student supervised by Prof. Dr. Jan Peters and Univ.-Prof. Dr. Elmar Rueckert from 10/2015 to 12/2020.
Hochschulstr. 10,
64289 Darmstadt,
Deutschland

Email:
   daniel@robot-learning.de
Web: https://www.rob.uni-luebeck.de/index.php?id=460

Publcations

2021

Tanneberg, Daniel; Ploeger, Kai; Rueckert, Elmar; Peters, Jan

SKID RAW: Skill Discovery from Raw Trajectories Journal Article

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.).

Links | BibTeX

SKID RAW: Skill Discovery from Raw Trajectories

2020

Tanneberg, Daniel; Rueckert, Elmar; Peters, Jan

Evolutionary training and abstraction yields algorithmic generalization of neural computers Journal Article

Nature Machine Intelligence, pp. 1–11, 2020.

Links | BibTeX

Evolutionary training and abstraction yields algorithmic generalization of neural computers

2019

Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar

Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks Journal Article

Neural Networks – Elsevier, 109 , pp. 67-80, 2019, ISBN: 0893-6080, (Impact Factor of 7.197 (2017)).

Links | BibTeX

Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks

2017

Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar

Efficient Online Adaptation with Stochastic Recurrent Neural Networks Inproceedings

Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017.

Links | BibTeX

Efficient Online Adaptation with Stochastic Recurrent Neural Networks

Thiem, Simon; Stark, Svenja; Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar

Simulation of the underactuated Sake Robotics Gripper in V-REP Inproceedings

Workshop at the International Conference on Humanoid Robots (HUMANOIDS), 2017.

Links | BibTeX

Simulation of the underactuated Sake Robotics Gripper in V-REP

Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar

Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals Inproceedings

Proceedings of the Conference on Robot Learning (CoRL), 2017.

Links | BibTeX

Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals

2016

Tanneberg, Daniel; Paraschos, Alexandros; Peters, Jan; Rueckert, Elmar

Deep Spiking Networks for Model-based Planning in Humanoids Inproceedings

Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2016.

Links | BibTeX

Deep Spiking Networks for Model-based Planning in Humanoids

Rueckert, Elmar; Kappel, David; Tanneberg, Daniel; Pecevski, Dejan; Peters, Jan

Recurrent Spiking Networks Solve Planning Tasks Journal Article

Nature Publishing Group: Scientific Reports, 6 (21142), 2016.

Links | BibTeX

Recurrent Spiking Networks Solve Planning Tasks

Sharma, David; Tanneberg, Daniel; Grosse-Wentrup, Moritz; Peters, Jan; Rueckert, Elmar

Adaptive Training Strategies for BCIs Inproceedings

Cybathlon Symposium, 2016.

Links | BibTeX

Adaptive Training Strategies for BCIs

2015

Tanneberg, Daniel

Spiking Neural Networks Solve Robot Planning Problems Technical Report

Technische Universität Darmstadt M.Sc. Thesis, 2015.

Links | BibTeX

Spiking Neural Networks Solve Robot Planning Problems