Publications

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2021

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

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

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

Links | BibTeX | Tags: mobile navigation, Probabilistic Inference

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

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 | Tags: Manipulation, movement primitives, Probabilistic Inference

SKID RAW: Skill Discovery from Raw Trajectories

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

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

Links | BibTeX | Tags: human motor control, intrinsic motivation, movement primitives, Probabilistic Inference, Reinforcement Learning, spiking

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

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 | Tags: neural network, Probabilistic Inference, RNN, spiking

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

2018

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

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

Journal of Machine Learning Research (JMLR), 19 (69), pp. 1-45, 2018.

Links | BibTeX | Tags: movement primitives, Probabilistic Inference, Simulation

Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling

2015

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

Extracting Low-Dimensional Control Variables for Movement Primitives Inproceedings

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

Links | BibTeX | Tags: movement primitives, Probabilistic Inference

Extracting Low-Dimensional Control Variables for Movement Primitives

2014

Rueckert, Elmar

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

Technical University Graz, 2014.

Links | BibTeX | Tags: graphical models, locomotion, model learning, morphological compuation, movement primitives, policy search, postural control, Probabilistic Inference, Reinforcement Learning, RNN, SOC, spiking

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

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

Links | BibTeX | Tags: policy search, Probabilistic Inference, SOC

Robust Policy Updates for Stochastic Optimal Control

2013

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

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

Frontiers in Computational Neuroscience, 6 (97), 2013.

Links | BibTeX | Tags: graphical models, movement primitives, Probabilistic Inference

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

2012

Rueckert, Elmar; Neumann, Gerhard

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

Artificial Life, 19 (1), 2012.

Links | BibTeX | Tags: morphological compuation, Probabilistic Inference, SOC

Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation

2011

Rueckert, Elmar; Neumann, Gerhard

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

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

Links | BibTeX | Tags: graphical models, morphological compuation, Probabilistic Inference, SOC

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.

Links | BibTeX | Tags: Probabilistic Inference

Simultaneous localisation and mapping for mobile robots with recent sensor technologies

Compact List without Images

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Journal Articles

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

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

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

Links | BibTeX

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

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

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

Journal of Machine Learning Research (JMLR), 19 (69), pp. 1-45, 2018.

Links | BibTeX

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

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

Frontiers in Computational Neuroscience, 6 (97), 2013.

Links | BibTeX

Rueckert, Elmar; Neumann, Gerhard

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

Artificial Life, 19 (1), 2012.

Links | BibTeX

Inproceedings

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

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

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

Links | BibTeX

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

Extracting Low-Dimensional Control Variables for Movement Primitives Inproceedings

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

Links | BibTeX

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

Robust Policy Updates for Stochastic Optimal Control Inproceedings

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

Links | BibTeX

Rueckert, Elmar; Neumann, Gerhard

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

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

Links | BibTeX

Masters Theses

Rueckert, Elmar

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

Technical University Graz, 2010.

Links | BibTeX

PhD Theses

Rueckert, Elmar

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

Technical University Graz, 2014.

Links | BibTeX