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Clemens Fritze, B.Sc.

Student Assistant at the Montanuniversität Leoben

Foto_20220621

Short bio: Clemens Fritze, B.Sc B.Sc started at CPS in July 2022.

Clemens Fritze is a master student in Mechatronics at the Johannes Kepler Universität Linz. Prior to his master program he studied Mechanical Engineering at the Montanuniversität Leoben, where he passed his Bachelor defense in May 2022. In 2018, Mr. Fritze finished a Bachelor study in Business Informatics at the university of applied science in Vienna (german FH Technikum Wien).

Research Interests

  • Robotics

Thesis

Contact

Clemens Fritze, B.Sc B.Sc
Student Assistent at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Email:   clemens.fritze@stud.unileoben.ac.at




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

CV of M.Sc. Daniel Tanneberg
GitHub
Google Citations
ORCID
Research Gate
Twitter

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, vol. 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, vol. 6, no. 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




Dr. Nils Rottmann

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

2022

Dave, Vedant; Rueckert, Elmar

Can we infer the full-arm manipulation skills from tactile targets? Workshop

International Conference on Humanoid Robots (Humanoids 2022), 2022.

Abstract | Links | BibTeX

Can we infer the full-arm manipulation skills from tactile targets?

Nwankwo, Linus; Fritze, Clemens; Bartsch, Konrad; Rueckert, Elmar

O2S: Open-source open shuttle Journal Article

In: Arxiv, 2022.

Abstract | Links | BibTeX

O2S: Open-source open shuttle

Xue, Honghu; Song, Rui; Petzold, Julian; Hein, Benedikt; Hamann, Heiko; Rueckert, Elmar

End-To-End Deep Reinforcement Learning for First-Person Pedestrian Visual Navigation in Urban Environments Inproceedings

In: International Conference on Humanoid Robots (Humanoids 2022), 2022.

Abstract | Links | BibTeX

End-To-End Deep Reinforcement Learning for First-Person Pedestrian Visual Navigation in Urban Environments

Dave, Vedant; Rueckert, Elmar

Predicting full-arm grasping motions from anticipated tactile responses Inproceedings

In: International Conference on Humanoid Robots (Humanoids 2022), 2022.

Abstract | Links | BibTeX

Predicting full-arm grasping motions from anticipated tactile responses

Herzog, Rebecca; Berger, Till M; Pauly, Martje Gesine; Xue, Honghu; Rueckert, Elmar; Munchau, Alexander; B"aumer, Tobias; Weissbach, Anne

Cerebellar transcranial current stimulation-an intraindividual comparison of different techniques Journal Article

In: Frontiers in Neuroscience, 2022.

Links | BibTeX

Cerebellar transcranial current stimulation-an intraindividual comparison of different techniques

Rottmann, Nils; Studt, Nico; Ernst, Floris; Rueckert, Elmar

ROS-Mobile: An Android™ application for the Robot Operating System Journal Article

In: Arxiv, 2022.

Links | BibTeX

ROS-Mobile: An Android™ application for the Robot Operating System

Xue, Honghu; Hein, Benedikt; Bakr, Mohamed; Schildbach, Georg; Abel, Bengt; Rueckert, Elmar

Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics Journal Article

In: Applied Sciences (MDPI), Special Issue on Intelligent Robotics, 2022, (Supplement: https://cloud.cps.unileoben.ac.at/index.php/s/Sj68rQewnkf4ppZ).

Abstract | Links | BibTeX

Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics

Leonel, Rozo; Vedant, Dave

Orientation Probabilistic Movement Primitives on Riemannian Manifolds Conference

Conference on Robot Learning (CoRL), vol. 164, 2022.

Abstract | Links | BibTeX

Orientation Probabilistic Movement Primitives on Riemannian Manifolds

2021

Denz, R.; Demirci, R.; Cansev, E.; Bliek, A.; Beckerle, P.; Rueckert, E.; Rottmann, N.

A high-accuracy, low-budget Sensor Glove for Trajectory Model Learning Inproceedings

In: International Conference on Advanced Robotics , pp. 7, 2021.

Links | BibTeX

A high-accuracy, low-budget Sensor Glove for Trajectory Model Learning

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

Xue, Honghu; Herzog, Rebecca; Berger, Till M.; Bäumer, Tobias; Weissbach, Anne; Rueckert, Elmar

Using Probabilistic Movement Primitives in analyzing human motion differences under Transcranial Current Stimulation Journal Article

In: Frontiers in Robotics and AI , vol. 8, 2021, ISSN: 2296-9144.

Abstract | Links | BibTeX

Using Probabilistic Movement Primitives in analyzing human motion differences under Transcranial Current Stimulation

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

Jamsek, Marko; Kunavar, Tjasa; Bobek, Urban; Rueckert, Elmar; Babic, Jan

Predictive exoskeleton control for arm-motion augmentation based on probabilistic movement primitives combined with a flow controller 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

Predictive exoskeleton control for arm-motion augmentation based on probabilistic movement primitives combined with a flow controller

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

Busch, Leander

Learning Motion Models for Local Path Planning Strategies Technical Report

University of Luebeck B.Sc. Thesis, 2021.

Links | BibTeX

Learning Motion Models for Local Path Planning Strategies

2020

Akbulut, M Tuluhan; Oztop, Erhan; Seker, M Yunus; Xue, Honghu; Tekden, Ahmet E; Ugur, Emre

ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing Conference

2020.

Abstract | Links | BibTeX

ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing

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

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

Overloepper, Phillip

An exploration scheme based on the state-action novelty in continuous state-action space Technical Report

University of Luebeck B.Sc. Thesis, 2020.

Links | BibTeX

An exploration scheme based on the state-action novelty in continuous state-action space

Tolga-Can Çallar, Elmar Rueckert; Böttger, Sven

Efficient Body Registration Using Single-View Range Imaging and Generic Shape Templates Inproceedings

In: 54th Annual Conference of the German Society for Biomedical Engineering (BMT 2020), 2020.

Links | BibTeX

Efficient Body Registration Using Single-View Range Imaging and Generic Shape Templates

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

Cartoni, E.; Mannella, F.; Santucci, V. G.; Triesch, J.; Rueckert, E.; Baldassarre, G.

REAL-2019: Robot open-Ended Autonomous Learning competition Journal Article

In: Proceedings of Machine Learning Research, vol. 123, pp. 142-152, 2020, (NeurIPS 2019 Competition and Demonstration Track).

Links | BibTeX

REAL-2019: Robot open-Ended Autonomous Learning competition

2019

Denz, Robin

Komplett abdeckende Pfadplanung fur kostengünstige Roboter Technical Report

Universität zu Lübeck B.Sc. Thesis, 2019.

Links | BibTeX

Komplett abdeckende Pfadplanung fur kostengünstige Roboter

Daibert, Viktor

Automatisierte Echtzeit-3D-Rekonstruktion auf mobilen Geräten Technical Report

Universität zu Lübeck M.Sc. Thesis, 2019.

Links | BibTeX

Automatisierte Echtzeit-3D-Rekonstruktion auf mobilen Geräten

Werner, Franz Johannes Michael

HIBO: Hierarchical Acquisition Functions for Bayesian Optimization Technical Report

Universität zu Lübeck 2019.

Links | BibTeX

HIBO: Hierarchical Acquisition Functions for Bayesian Optimization

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

Boettger, S.; Callar, T. C.; Schweikard, A.; Rueckert, E.

Medical robotics simulation framework for application-specific optimal kinematics Inproceedings

In: Current Directions in Biomedical Engineering 2019, pp. 1–5, 2019.

Links | BibTeX

Medical robotics simulation framework for application-specific optimal kinematics

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

Walter, Alexander

Machine Learning for plant classification based on chlorophyll detection Technical Report

University of Luebeck B.Sc. Thesis, 2019.

Links | BibTeX

Machine Learning for plant classification based on chlorophyll detection

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

Rueckert, Elmar; Jauer, Philipp; Derksen, Alexander; Schweikard, Achim

Dynamic Control Strategies for Cable-Driven Master Slave Robots Inproceedings

In: Keck, Tobias (Ed.): Proceedings on Minimally Invasive Surgery, Luebeck, Germany, 2019, (January 24-25, 2019).

Links | BibTeX

Dynamic Control Strategies for Cable-Driven Master Slave Robots

Uhlenberg, Jan; Risler, Wolfgang; Rueckert, Elmar

Development of a high-performance and low-cost fraction collector Technical Report

Universität zu Lübeck M.Sc. Report, 2019.

Links | BibTeX

Development of a high-performance and low-cost fraction collector

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, vol. 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

2018

Çallar, Tolga-Can

Design of a Simulation Framework for the Exploration of Kinematic Structures for Robotic Ultrasound Imaging Technical Report

Universität zu Lübeck B.Sc. Thesis, 2018.

Links | BibTeX

Design of a Simulation Framework for the Exploration of Kinematic Structures for Robotic Ultrasound Imaging

Thiede, Clara

Construction of a universal mounting adapter for various types of ultrasound probes on a robot ange (using CAD and 3D printing) Technical Report

Universität zu Lübeck B.Sc. Thesis, 2018.

Links | BibTeX

Gondaliya, Kaushikkumar D.; Peters, Jan; Rueckert, Elmar

Learning to Categorize Bug Reports with LSTM Networks Inproceedings

In: Proceedings of the International Conference on Advances in System Testing and Validation Lifecycle (VALID)., pp. 6, XPS (Xpert Publishing Services), Nice, France, 2018, ISBN: 978-1-61208-671-2, ( October 14-18, 2018).

Links | BibTeX

Learning to Categorize Bug Reports with LSTM Networks

Sosic, Adrian; Zoubir, Abdelhak M.; Rueckert, Elmar; Peters, Jan; Koeppl, Heinz

Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling Journal Article

In: Journal of Machine Learning Research (JMLR), vol. 19, no. 69, pp. 1-45, 2018.

Links | BibTeX

Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling

Dittmar, Denny

Distributed Reinforcement Learning with Neural Networks for Robotics Technical Report

Technische Universität Darmstadt M.Sc. Thesis, 2018.

Links | BibTeX

Distributed Reinforcement Learning with Neural Networks for Robotics

Paraschos, Alexandros; Rueckert, Elmar; Peters, Jan; Neumann, Gerhard

Probabilistic Movement Primitives under Unknown System Dynamics Journal Article

In: Advanced Robotics (ARJ), vol. 32, no. 6, pp. 297-310, 2018.

Links | BibTeX

Probabilistic Movement Primitives under Unknown System Dynamics

2017

Frisch, Yannik

The Effects of Intrinsic Motivation Signals on Reinforcement Learning Strategies Technical Report

Technische Universität Darmstadt B.Sc. Thesis, 2017.

Links | BibTeX

The Effects of Intrinsic Motivation Signals on Reinforcement Learning Strategies

Rueckert, Elmar; Nakatenus, Moritz; Tosatto, Samuele; Peters, Jan

Learning Inverse Dynamics Models in O(n) time with LSTM networks Inproceedings

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

Links | BibTeX

Learning Inverse Dynamics Models in O(n) time with LSTM networks

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

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

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

Pfanschilling, Viktor

Self-Programming Mutation and Crossover in Genetic Programming for Code Generation Technical Report

B.Sc. Thesis, 2017.

Links | BibTeX

Self-Programming Mutation and Crossover in Genetic Programming for Code Generation

Thiem, Simon-Konstantin

Simulation of the underactuated Sake Robotics Gripper in V-REP and ROS Technical Report

Technische Universität Darmstadt B.Sc. Thesis, 2017.

Links | BibTeX

Simulation of the underactuated Sake Robotics Gripper in V-REP and ROS

Nakatenus, Moritz

LSTM Networks for movement planning in humanoids Technical Report

Technische Universität Darmstadt M.Sc. Project, 2017.

BibTeX

LSTM Networks for movement planning in humanoids

Gondaliya, Kaushik

Learning to Categorize Issues in Distributed Bug Tracker Systems Technical Report

Technische Universität Darmstadt M.Sc. Thesis, 2017.

Links | BibTeX

Learning to Categorize Issues in Distributed Bug Tracker Systems

Polat, Harun

Nonparametric deep neural networks for movement planning Technical Report

Technische Universität Darmstadt B.Sc. Thesis, 2017.

Links | BibTeX

Nonparametric deep neural networks for movement planning

Plage, Lena

Reinforcement Learning for tactile-based finger gaiting Technical Report

Technische Universität Darmstadt B.Sc. Thesis, 2017.

Links | BibTeX

Reinforcement Learning for tactile-based finger gaiting

Sharma, David

Adaptive Training Strategies for Brain-Computer-Interfaces Technical Report

Technische Universität Darmstadt M.Sc. Thesis, 2017.

Links | BibTeX

Adaptive Training Strategies for Brain-Computer-Interfaces

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

Azad, Morteza; Ortenzi, Valerio; Lin, Hsiu-Chin; Rueckert, Elmar; Mistry, Michael

Model Estimation and Control of Complaint Contact Normal Force Inproceedings

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

Links | BibTeX

Model Estimation and Control of Complaint Contact Normal Force

Smyk, Mike

Model-based Control and Planning for Real Robots Technical Report

Technische Universität Darmstadt M.Sc. Project, 2016.

Links | BibTeX

Model-based Control and Planning for Real Robots

Rueckert, Elmar; Camernik, Jernej; Peters, Jan; Babic, Jan

Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control Journal Article

In: Nature Publishing Group: Scientific Reports, vol. 6, no. 28455, 2016.

Links | BibTeX

Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control

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

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

Recurrent Spiking Networks Solve Planning Tasks Journal Article

In: Nature Publishing Group: Scientific Reports, vol. 6, no. 21142, 2016.

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Recurrent Spiking Networks Solve Planning Tasks

Kohlschuetter, Jan

Learning Probabilistic Classifiers from Electromyography Data for Predicting Knee Abnormalities Technical Report

Technische Universität Darmstadt M.Sc. Thesis, 2016.

Links | BibTeX

Learning Probabilistic Classifiers from Electromyography Data for Predicting Knee Abnormalities

Kohlschuetter, Jan; Peters, Jan; Rueckert, Elmar

Learning Probabilistic Features from EMG Data for Predicting Knee Abnormalities Inproceedings

In: Proceedings of the XIV Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON), 2016.

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Learning Probabilistic Features from EMG Data for Predicting Knee Abnormalities

Modugno, Valerio; Neumann, Gerhard; Rueckert, Elmar; Oriolo, Giuseppe; Peters, Jan; Ivaldi, Serena

Learning soft task priorities for control of redundant robots Inproceedings

In: Proceedings of the International Conference on Robotics and Automation (ICRA), 2016.

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Learning soft task priorities for control of redundant robots

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

Adaptive Training Strategies for BCIs Inproceedings

In: Cybathlon Symposium, 2016.

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Adaptive Training Strategies for BCIs

Weber, Paul; Rueckert, Elmar; Calandra, Roberto; Peters, Jan; Beckerle, Philipp

A Low-cost Sensor Glove with Vibrotactile Feedback and Multiple Finger Joint and Hand Motion Sensing for Human-Robot Interaction Inproceedings

In: Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2016.

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A Low-cost Sensor Glove with Vibrotactile Feedback and Multiple Finger Joint and Hand Motion Sensing for Human-Robot Interaction

2015

Tanneberg, Daniel

Spiking Neural Networks Solve Robot Planning Problems Technical Report

Technische Universität Darmstadt M.Sc. Thesis, 2015.

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Spiking Neural Networks Solve Robot Planning Problems

Calandra, Roberto; Ivaldi, Serena; Deisenroth, Marc; Rueckert, Elmar; Peters, Jan

Learning Inverse Dynamics Models with Contacts Inproceedings

In: Proceedings of the International Conference on Robotics and Automation (ICRA), 2015.

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Learning Inverse Dynamics Models with Contacts

Rueckert, Elmar; Mundo, Jan; Paraschos, Alexandros; Peters, Jan; Neumann, Gerhard

Extracting Low-Dimensional Control Variables for Movement Primitives Inproceedings

In: Proceedings of the International Conference on Robotics and Automation (ICRA), 2015.

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Extracting Low-Dimensional Control Variables for Movement Primitives

Paraschos, Alexandros; Rueckert, Elmar; Peters, Jan; Neumann, Gerhard

Model-Free Probabilistic Movement Primitives for Physical Interaction Inproceedings

In: Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2015.

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Model-Free Probabilistic Movement Primitives for Physical Interaction

Rueckert, Elmar; Lioutikov, Rudolf; Calandra, Roberto; Schmidt, Marius; Beckerle, Philipp; Peters, Jan

Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations Inproceedings

In: ICRA 2015 Workshop on Tactile and force sensing for autonomous compliant intelligent robots, 2015.

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Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations

2014

Mindt, Max

Probabilistic Inference for Movement Planning in Humanoids Technical Report

Technische Universität Darmstadt M.Sc. Thesis, 2014.

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Probabilistic Inference for Movement Planning in Humanoids

Mundo, Jan

Structure Learning for Movement Primitives Technical Report

Technische Universität Darmstadt M.Sc. Thesis, 2014.

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Structure Learning for Movement Primitives

Rueckert, Elmar

Biologically inspired motor skill learning in robotics through probabilistic inference PhD Thesis

Technical University Graz, 2014.

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Biologically inspired motor skill learning in robotics through probabilistic inference

Rueckert, Elmar; Mindt, Max; Peters, Jan; Neumann, Gerhard

Robust Policy Updates for Stochastic Optimal Control Inproceedings

In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2014.

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Robust Policy Updates for Stochastic Optimal Control

2013

Rueckert, Elmar; d'Avella, Andrea

Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems Journal Article

In: Frontiers in Computational Neuroscience, vol. 7, no. 138, 2013.

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Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems

Kniewasser, Gerhard

Reinforcement Learning with Dynamic Movement Primitives - DMPs Technical Report

Technische Universität Graz M.Sc. Project, 2013.

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Reinforcement Learning with Dynamic Movement Primitives - DMPs

Prevenhueber, Oliver

Monte Carlo Sampling Methods for Motor Control of Constraint High-dimensional Systems Technical Report

Technische Universität Graz M.Sc. Thesis, 2013.

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Monte Carlo Sampling Methods for Motor Control of Constraint High-dimensional Systems

Gsenger, Othmar

Probabilistic Models for Learning the Dynamics Model of Robot Technical Report

Technische Universität Graz M.Sc. Thesis, 2013.

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Probabilistic Models for Learning the Dynamics Model of Robot

Rueckert, Elmar; Neumann, Gerhard; Toussaint, Marc; Maass, Wolfgang

Learned graphical models for probabilistic planning provide a new class of movement primitives Journal Article

In: Frontiers in Computational Neuroscience, vol. 6, no. 97, 2013.

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 Learned graphical models for probabilistic planning provide a new class of movement primitives

Rueckert, Elmar; d'Avella, Andrea

Learned Muscle Synergies as Prior in Dynamical Systems for Controlling Bio-mechanical and Robotic Systems Inproceedings

In: Abstracts of Neural Control of Movement Conference (NCM), Conference Talk, pp. 27–28, 2013.

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Learned Muscle Synergies as Prior in Dynamical Systems for Controlling Bio-mechanical and Robotic Systems

2012

Rueckert, Elmar; Neumann, Gerhard

Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation Journal Article

In: Artificial Life, vol. 19, no. 1, 2012.

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Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation

Prevenhueber, Oliver

Gibbs Sampling Methods for Motor Control Problems with Hard Constraints Technical Report

Technische Universität Graz 2012.

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Gibbs Sampling Methods for Motor Control Problems with Hard Constraints

2011

Genewein, Tim

Structure Learning for Robotic Motor Control Technical Report

Technische Universität Graz M.Sc. Thesis, 2011.

BibTeX

Wiesner, Thomas

Ein Vergleich von Lernalgorithmen für Parametersuche im hochdimensionalen Raum Technical Report

Technische Universität Graz B.Sc. Thesis, 2011.

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Ein Vergleich von Lernalgorithmen für Parametersuche im hochdimensionalen Raum

Rueckert, Elmar; Neumann, Gerhard

A study of Morphological Computation by using Probabilistic Inference for Motor Planning Inproceedings

In: Proceedings of the 2nd International Conference on Morphological Computation (ICMC), pp. 51–53, 2011.

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A study of Morphological Computation by using Probabilistic Inference for Motor Planning

2010

Rueckert, Elmar

Simultaneous localisation and mapping for mobile robots with recent sensor technologies Masters Thesis

Technical University Graz, 2010.

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Simultaneous localisation and mapping for mobile robots with recent sensor technologies

0000

Benedikt Hein Honghu Xue, Mohamed Bakr; Rueckert, Elmar

[No title] Journal Article Forthcoming

In: MDPI special issue "Intelligent Robotics", Forthcoming.

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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