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

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.).

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

In: 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

In: 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

In: 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

In: 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

In: 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

In: 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

In: 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

In: 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

Svenja Stark, M.Sc.

Ph.D. Student at the Technical University of Darmstadt

Portrait of Svenja Stark, Jan. 2018

Short bio: Svenja Stark left the TU Darmstadt team in 2020 and is now a successful high school teacher in Hessen. She joined the Intelligent Autonomous Systems Group as a PhD student in December 2016, where she was supervised by Prof. Dr. Jan Peters and Univ.-Prof. Dr. Elmar Rueckert. 

She has been working on the GOAL-Robots project that aimed at developing goal-based open-ended autonomous learning robots; building lifelong learning robots.

Before joining the Autonomous Systems Labs, Svenja Stark received a Bachelor and a Master of Science degree in Computer Science from the TU Darmstadt. During her studies, she completed parts of her graduate coursework at the University of Massachusetts in Amherst. Her thesis entitled “Learning Probabilistic Feedforward and Feedback Policies for Generating Stable Walking Behaviors” was written under supervision of Elmar Rueckert and Jan Peters.

Research Interests

  • Multi-task learning, meta-learning, goal-based learning, intrinsic motivation, lifelong learning, Reinforcement Learning, motor skill learning.

Contact & Quick Links

M.Sc. Svenja Stark
Doctoral Student supervised by Prof. Dr. Jan Peters and Univ.-Prof. Dr. Elmar Rueckert. 
Hochschulstr. 10,
64289 Darmstadt,
Deutschland

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

Publcations

2019

Stark, Svenja; Peters, Jan; Rueckert, Elmar

Experience Reuse with Probabilistic Movement Primitives Inproceedings

In: Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2019., 2019.

Links | BibTeX

Experience Reuse with Probabilistic Movement Primitives

2017

Stark, Svenja; Peters, Jan; Rueckert, Elmar

A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries Inproceedings

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

Links | BibTeX

A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries

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

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

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

Links | BibTeX

Simulation of the underactuated Sake Robotics Gripper in V-REP

2016

Stark, Svenja

Learning Probabilistic Feedforward and Feedback Policies for Stable Walking Technical Report

Technische Universität Darmstadt M.Sc. Thesis, 2016.

Links | BibTeX

Learning Probabilistic Feedforward and Feedback Policies for Stable Walking

Nils Rottmann, M.Sc.

Ph.D. Student at the University of Luebeck

Short bio: With January 2018, Nils Rottmann is a PhD student and research scientist at the Institute for Robotics and Cognitive Systems at the University of Luebeck. In his doctoral study, with the title “Smart Sensor, Navigation and Learning Strategies for low-cost lawn care Systems”, he develops low-cost sensor systems and investigates probabilistic learning and modeling approaches. His research addresses the challenges of learning adaptive control strategies from few and sparse data and to predict and plan complex motions in dynamical systems.

He holds a master’s degree in Theoretical Mechanical Engineering from the Hamburg University of Technology, Germany. Nils Rottmann graduated with honors in December 2017 with a thesis entitled „Geometric Control and Stochastic Trajectory Planning for Underwater Robotic Systems“.

Research Interests

  • Robotics: Mobile Robotics, Sensor Development, Robot-Operating-System (ROS), Mobile Navigation, Path Planning, Complete Coverage Path Planning, Probabilistic Robotics.
  • Machine Learning: Non-Linear Regression, Graphical Models, Probabilistic Inference, Variational Inference, Gaussian Processes, Bayesian Optimization.

Contact & Quick Links

M.Sc. Nils Rottmann
Doctoral Student supervised by Univ.-Prof. Dr. Elmar Rueckert since March 2018.
Ratzeburger Allee 160,
23562 Lübeck,
Deutschland

Phone:  +49 451 3101 – 5222 
Email:   rottmann@rob.uni-luebeck.de
Web:  https://nrottmann.github.io

CV of M.Sc. Nils Rottmann
DBLP
Frontiers Network
GitHub
Google Citations
LinkedIn
ORCID
Research Gate

Publcations

2021

Rottmann, N.; Denz, R.; Bruder, R.; Rueckert, E.

Probabilistic Approach for Complete Coverage Path Planning with low-cost Systems Inproceedings

In: European Conference on Mobile Robots (ECMR 2021), 2021.

Links | BibTeX

Probabilistic Approach for Complete Coverage Path Planning with low-cost Systems

Cansev, Mehmet Ege; Xue, Honghu; Rottmann, Nils; Bliek, Adna; Miller, Luke E.; Rueckert, Elmar; Beckerle, Philipp

Interactive Human-Robot Skill Transfer: A Review of Learning Methods and User Experience Journal Article

In: Advanced Intelligent Systems, pp. 1–28, 2021.

Links | BibTeX

Interactive Human-Robot Skill Transfer: A Review of Learning Methods and User Experience

2020

Rottmann, N.; Bruder, R.; Schweikard, A.; Rueckert, E.

Exploiting Chlorophyll Fluorescense for Building Robust low-Cost Mowing Area Detectors Inproceedings

In: IEEE SENSORS , pp. 1–4, 2020.

Links | BibTeX

Exploiting Chlorophyll Fluorescense for Building Robust low-Cost Mowing Area Detectors

Rottmann, N.; Kunavar, T.; Babič, J.; Peters, J.; Rueckert, E.

Learning Hierarchical Acquisition Functions for Bayesian Optimization Inproceedings

In: International Conference on Intelligent Robots and Systems (IROS’ 2020), 2020.

Links | BibTeX

Learning Hierarchical Acquisition Functions for Bayesian Optimization

Rottmann, N.; Bruder, R.; Xue, H.; Schweikard, A.; Rueckert, E.

Parameter Optimization for Loop Closure Detection in Closed Environments Inproceedings

In: Workshop Paper at the International Conference on Intelligent Robots and Systems (IROS), pp. 1–8, 2020.

Links | BibTeX

Parameter Optimization for Loop Closure Detection in Closed Environments

Rottmann, N.; Bruder, R.; Schweikard, A.; Rueckert, E.

A novel Chlorophyll Fluorescence based approach for Mowing Area Classification Journal Article

In: IEEE Sensors Journal, 2020.

Links | BibTeX

A novel Chlorophyll Fluorescence based approach for Mowing Area Classification

Xue, H.; Boettger, S.; Rottmann, N.; Pandya, H.; Bruder, R.; Neumann, G.; Schweikard, A.; Rueckert, E.

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks Inproceedings

In: International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020.

Links | BibTeX

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks

2019

Rottmann, N.; Bruder, R.; Schweikard, A.; Rueckert, E.

Loop Closure Detection in Closed Environments Inproceedings

In: European Conference on Mobile Robots (ECMR 2019), 2019, ISBN: 978-1-7281-3605-9.

Links | BibTeX

Loop Closure Detection in Closed Environments

Rottmann, N.; Bruder, R.; Schweikard, A.; Rueckert, E.

Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors Inproceedings

In: Proceedings of International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS), Prague, Czech Republic , 2019, ( February 22-24, 2019).

Links | BibTeX

Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors

Mrs. Mag. Elenka Orszova

Secretrary

Short bio: Mrs. Mag. B.Sc. Elenka Orszova  started in August 2021 at the chair of CPS. 

She studied Anthropology and Cognitive Science at the Comenius University in Bratislava, Slovakia. 

Research Interests

  • Cyber-Physical-Systems 
  • Modern Technologies 
  • Learning Machines and Robotics

Contact

Mrs. Mag. Elenka Orszova
Sekretariat des Lehrstuhls für Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Phone:  +43 3842 402 – 1901
Email:   elenka.orszova@unileoben.ac.at 
Web:  https://cps.unileoben.ac.at