A

Abdulsamad, Hany (2022):
Statistical Machine Learning for Modeling and Control of Stochastic Structured Systems. (Publisher's Version)
Darmstadt, Technische Universität Darmstadt,
DOI: 10.26083/tuprints-00022573,
[Ph.D. Thesis]

Abi-Farraj, Firas ; Pacchierotti, Claudio ; Arenz, Oleg ; Neumann, Gerhard ; Robuffo Giordano, Paolo (2022):
A Haptic Shared-Control Architecture for Guided Multi-Target Robotic Grasping. (Postprint)
In: IEEE Transactions on Haptics, 13 (2), pp. 270-285. IEEE, ISSN 1939-1412, e-ISSN 2329-4051,
DOI: 10.26083/tuprints-00022928,
[Article]

Akrour, Riad ; Pajarinen, Joni ; Peters, Jan ; Neumann, Gerhard (2022):
Projections for Approximate Policy Iteration Algorithms. (Publisher's Version)
In: Proceedings of Machine Learning Research, 97, In: Proceedings of the 36th International Conference on Machine Learning, pp. 181-190,
Darmstadt, PMLR, 36th International Conference on Machine Learning, Long Beach, California, USA, 09.-15.06.2019, DOI: 10.26083/tuprints-00020582,
[Conference or Workshop Item]

Arenz, Julian Oleg (2021):
Sample-Efficient I-Projections for Robot Learning. (Publisher's Version)
Darmstadt, Technische Universität,
DOI: 10.12921/tuprints-00014271,
[Ph.D. Thesis]

Arenz, Oleg ; Abdulsamad, Hany ; Neumann, Gerhard (2022):
Optimal Control and Inverse Optimal Control by Distribution Matching. (Postprint)
In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4046-4053,
Darmstadt, IEEE, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea, 09.-14.10.2016, e-ISSN 2153-0866, ISBN 978-1-5090-3762-9,
DOI: 10.26083/tuprints-00022929,
[Conference or Workshop Item]

Arenz, Oleg ; Neumann, Gerhard ; Zhong, Mingjun (2022):
Efficient Gradient-Free Variational Inference using Policy Search. (Publisher's Version)
80, In: Proceedings of Machine Learning Research, pp. 234-243,
Darmstadt, PMLR, 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden, 10.-15.07.2018, e-ISSN 2640-3498,
DOI: 10.26083/tuprints-00022925,
[Conference or Workshop Item]

Arenz, Oleg ; Zhong, Mingjun ; Neumann, Gerhard (2022):
Trust-Region Variational Inference with Gaussian Mixture Models. (Publisher's Version)
In: Journal of Machine Learning Research, 21, JMLR, e-ISSN 1533-7928,
DOI: 10.26083/tuprints-00022920,
[Article]

B

Becker, Philipp ; Arenz, Oleg ; Neumann, Gerhard (2022):
Expected Information Maximization: Using the I-Projection for Mixture Density Estimation. (Publisher's Version)
Darmstadt, 8. International Conference on Learning Representations (ICLR 2020), Virtual Conference, 26.-30.04.2020, DOI: 10.26083/tuprints-00022969,
[Conference or Workshop Item]

Becker-Ehmck, Philip (2022):
Latent State-Space Models for Control. (Publisher's Version)
Darmstadt, Technische Universität Darmstadt,
DOI: 10.26083/tuprints-00022489,
[Ph.D. Thesis]

Belousov, Boris (2022):
On Optimal Behavior Under Uncertainty in Humans and Robots. (Publisher's Version)
Darmstadt, Technische Universität Darmstadt,
DOI: 10.26083/tuprints-00022561,
[Ph.D. Thesis]

Belousov, Boris ; Neumann, Gerhard ; Rothkopf, Constantin A. ; Peters, Jan (2022):
Catching heuristics are optimal control policies. (Publisher's Version)
In: Advances in Neural Information Processing Systems 29 : 30th Annual Conference on Neural Information Processing Systems 2016,
Darmstadt, Neural Information Processing Systems, Advances in Neural Information Processing Systems 29 (NIPS 2016), Barcelona, Spain, 05.-10.12.2016, DOI: 10.26083/tuprints-00020556,
[Conference or Workshop Item]

Belousov, Boris ; Peters, Jan (2019):
Entropic Regularization of Markov Decision Processes.
21, In: Entropy, (7), MDPI, ISSN 1099-4300,
[Article]

Belousov, Boris ; Sadybakasov, Alymbek ; Wibranek, Bastian ; Veiga, Filipe ; Tessmann, Oliver ; Peters, Jan (2022):
Building a Library of Tactile Skills Based on FingerVision. (Postprint)
In: 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), pp. 717-722,
Darmstadt, IEEE, 19th International Conference on Humanoid Robots (Humanoids), Toronto, ON, Canada, 15.-17.10.2019, e-ISSN 2164-0580, ISBN 978-1-5386-7630-1,
DOI: 10.26083/tuprints-00020548,
[Conference or Workshop Item]

Büchler, Dieter (2019):
Robot Learning for Muscular Systems. (Publisher's Version)
Darmstadt, Technische Universität,
DOI: 10.25534/tuprints-00017210,
[Ph.D. Thesis]

C

Calandra, Roberto (2017):
Bayesian Modeling for Optimization and Control in Robotics.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

D

Dam, Tuan (2023):
Sample Efficient Monte Carlo Tree Search for Robotics. (Publisher's Version)
Darmstadt, Technische Universität Darmstadt,
DOI: 10.26083/tuprints-00022931,
[Ph.D. Thesis]

Daniel, Christian (2016):
Learning Hierarchical Policies from Human Feedback.
Darmstadt, Technische Universität Darmstadt,
[Ph.D. Thesis]

Delfa Victoria, Juan Manuel (2016):
Automated Hierarchical, Forward-Chaining Temporal Planner for Planetary Robots Exploring Unknown Environments.
Darmstadt, Technische Universität Darmstadt,
[Ph.D. Thesis]

Dezfuli, Niloofar (2015):
Novel Interaction Concepts for Event Participation Through Social Television.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

E

Eilers, Christian ; Eschmann, Jonas ; Menzenbach, Robin ; Belousov, Boris ; Muratore, Fabio ; Peters, Jan (2022):
Underactuated Waypoint Trajectory Optimization for Light Painting Photography. (Postprint)
In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 1505-1510,
Darmstadt, IEEE, International Conference on Robotics and Automation (ICRA), Paris, France, 31.05-31.08.2020, e-ISSN 2577-087X, ISBN 978-1-7281-7395-5,
DOI: 10.26083/tuprints-00020549,
[Conference or Workshop Item]

Ewerton, Marco ; Arenz, Oleg ; Maeda, Guilherme ; Koert, Dorothea ; Kolev, Zlatko ; Takahashi, Masaki ; Peters, Jan (2019):
Learning Trajectory Distributions for Assisted Teleoperation and Path Planning.
In: Frontiers in Robotics and AI, 6, Frontiers, e-ISSN 2296-9144,
DOI: 10.25534/tuprints-00009657,
[Article]

Ewerton, Marco ; Arenz, Oleg ; Peters, Jan (2022):
Assisted teleoperation in changing environments with a mixture of virtual guides. (Postprint)
In: Advanced Robotics, 34 (18), pp. 1157-1170. Taylor & Francis, ISSN 0169-1864, e-ISSN 1568-5535,
DOI: 10.26083/tuprints-00023003,
[Article]

F

Fernandes Veiga, Filipe (2018):
Towards Dexterous In-Hand Manipulation through Tactile Sensing.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

G

Gebhardt, Gregor H.W. (2019):
Using Mean Embeddings for State Estimation and Reinforcement Learning.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

Gomez Gonzalez, Sebastian (2020):
Real Time Probabilistic Models for Robot Trajectories.
Darmstadt, Technische Universität Darmstadt,
DOI: 10.25534/tuprints-00011492,
[Ph.D. Thesis]

Gomez-Gonzalez, Sebastian ; Nemmour, Yassine ; Schölkopf, Bernhard ; Peters, Jan (2022):
Reliable Real-Time Ball Tracking for Robot Table Tennis. (Publisher's Version)
In: Robotics, 8 (4), MDPI, e-ISSN 2218-6581,
DOI: 10.26083/tuprints-00015740,
[Article]

K

Kober, Jens (2012):
Learning Motor Skills: From Algorithms to Robot Experiments.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

Koc, Okan (2018):
Optimal Trajectory Generation and Learning Control for Robot Table Tennis.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

Koert, Dorothea (2020):
Interactive Machine Learning for Assistive Robots. (Publisher's Version)
Darmstadt, Technische Universität,
DOI: 10.25534/tuprints-00014184,
[Ph.D. Thesis]

Koert, Dorothea ; Kircher, Maximilian ; Salikutluk, Vildan ; D'Eramo, Carlo ; Peters, Jan (2021):
Multi-Channel Interactive Reinforcement Learning for Sequential Tasks. (Publisher's Version)
In: Frontiers in Robotics and AI, 7, Frontiers, e-ISSN 2296-9144,
DOI: 10.26083/tuprints-00019239,
[Article]

Koert, Dorothea ; Maeda, Guilherme ; Lioutikov, Rudolf ; Neumann, Gerhard ; Peters, Jan (2022):
Demonstration based trajectory optimization for generalizable robot motions. (Postprint)
In: 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 515-522,
Darmstadt, IEEE, International Conference on Humanoid Robots (Humanoids), Cancun, Mexico, 15.-17.11.2016, e-ISSN 2164-0580, ISBN 978-1-509-04719-2,
DOI: 10.26083/tuprints-00020544,
[Conference or Workshop Item]

Koert, Dorothea ; Maeda, Guilherme ; Neumann, Gerhard ; Peters, Jan (2022):
Learning Coupled Forward-Inverse Models with Combined Prediction Errors. (Postprint)
In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2433-2439,
Darmstadt, IEEE, International Conference on Robotics and Automation (ICRA) 2018, Brisbane, QLD, Australia, 21.-25.05.2018, e-ISSN 2577-087X, ISBN 978-1-5386-3081-5,
DOI: 10.26083/tuprints-00020546,
[Conference or Workshop Item]

Koert, Dorothea ; Pajarinen, Joni ; Schotschneider, Albert ; Trick, Susanne ; Rothkopf, Constantin A. ; Peters, Jan (2022):
Learning Intention Aware Online Adaptation of Movement Primitives. (Postprint)
In: IEEE Robotics and Automation Letters, 4 (4), pp. 3719-3726. IEEE, e-ISSN 2377-3766,
DOI: 10.26083/tuprints-00020543,
[Article]

Koert, Dorothea ; Trick, Susanne ; Ewerton, Marco ; Lutter, Michael ; Peters, Jan (2022):
Online Learning of an Open-Ended Skill Library for Collaborative Tasks. (Postprint)
In: 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids),
Darmstadt, IEEE, International Conference on Humanoid Robots (Humanoids), Beijing, China, 06.-09.11.2018, e-ISSN 2164-0580, ISBN 978-1-5386-7283-9,
DOI: 10.26083/tuprints-00020545,
[Conference or Workshop Item]

L

Lampariello, Roberto (2021):
Optimal Motion Planning for Object Interception and Capture. (Publisher's Version)
Darmstadt, Technische Universität,
DOI: 10.26083/tuprints-00017617,
[Ph.D. Thesis]

Laux, Melvin ; Arenz, Oleg ; Peters, Jan ; Pajarinen, Joni (2022):
Deep Adversarial Reinforcement Learning for Object Disentangling. (Postprint)
In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5504-5510,
Darmstadt, IEEE, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA (Virtual), 25.10.-29.10.2020, e-ISSN 2153-0866, ISBN 978-1-7281-6212-6,
DOI: 10.26083/tuprints-00022926,
[Conference or Workshop Item]

Lioutikov, Rudolf (2018):
Parsing Motion and Composing Behavior for Semi-Autonomous Manipulation.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

Lioutikov, Rudolf ; Neumann, Gerhard ; Maeda, Guilherme ; Peters, Jan (2022):
Learning movement primitive libraries through probabilistic segmentation. (Postprint)
In: The International Journal of Robotics Research, 36 (8), pp. 879-894. SAGE Publications, ISSN 0278-3649, e-ISSN 1741-3176,
DOI: 10.26083/tuprints-00020539,
[Article]

Luck, Kevin Sebastian (2014):
Latent Space Reinforcement Learning.
Darmstadt, Technische Universität, [Bachelor Thesis]

Lutter, Michael (2021):
Inductive Biases in Machine Learning for Robotics and Control. (Publisher's Version)
Darmstadt, Technische Universität,
DOI: 10.26083/tuprints-00020048,
[Ph.D. Thesis]

Löckel, Stefan Alexander (2022):
Machine Learning for Modeling and Analyzing of Race Car Drivers. (Publisher's Version)
Darmstadt, Technische Universität,
DOI: 10.26083/tuprints-00020218,
[Ph.D. Thesis]

M

Manschitz, Simon (2018):
Learning Sequential Skills for Robot Manipulation Tasks.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

Merfels, Christian (2014):
Large-scale probabilistic feature mapping and tracking for autonomous driving.
Darmstadt, Technische Universität, [Master Thesis]

Moos, Janosch ; Hansel, Kay ; Abdulsamad, Hany ; Stark, Svenja ; Clever, Debora ; Peters, Jan (2022):
Robust Reinforcement Learning: A Review of Foundations and Recent Advances. (Publisher's Version)
In: Machine Learning and Knowledge Extraction, 4 (1), pp. 276-315. MDPI, e-ISSN 2504-4990,
DOI: 10.26083/tuprints-00021118,
[Article]

Muelling, Katharina (2013):
Modeling and Learning of Complex Motor Tasks: A Case Study with Robot Table Tennis.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

Muratore, Fabio (2021):
Randomizing Physics Simulations for Robot Learning. (Publisher's Version)
Darmstadt, Technische Universität,
DOI: 10.26083/tuprints-00019940,
[Ph.D. Thesis]

Muratore, Fabio ; Ramos, Fabio ; Turk, Greg ; Yu, Wenhao ; Gienger, Michael ; Peters, Jan (2022):
Robot Learning From Randomized Simulations: A Review. (Publisher's Version)
In: Frontiers in Robotics and AI, 9, Frontiers, e-ISSN 2296-9144,
DOI: 10.26083/tuprints-00021227,
[Article]

N

Nass, David ; Belousov, Boris ; Peters, Jan (2022):
Entropic Risk Measure in Policy Search. (Postprint)
In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1101-1106,
Darmstadt, IEEE, International Conference on Intelligent Robots and Systems (IROS), Macau, China, 03.-08.11.2019, e-ISSN 2153-0866, ISBN 978-1-7281-4004-9,
DOI: 10.26083/tuprints-00020551,
[Conference or Workshop Item]

P

Pajarinen, Joni ; Arenz, Oleg ; Peters, Jan ; Neumann, Gerhard (2022):
Probabilistic Approach to Physical Object Disentangling. (Postprint)
In: IEEE Robotics and Automation Letters, 5 (4), pp. 5510-5517. IEEE, e-ISSN 2377-3766,
DOI: 10.26083/tuprints-00022927,
[Article]

Pajarinen, Joni ; Thai, Hong Linh ; Akrour, Riad ; Peters, Jan ; Neumann, Gerhard (2022):
Compatible natural gradient policy search. (Publisher's Version)
In: Machine Learning, 108 (8-9), pp. 1443-1466. Springer, ISSN 0885-6125, e-ISSN 1573-0565,
DOI: 10.26083/tuprints-00020531,
[Article]

Paraschos, Alexandros (2017):
Robot Skill Representation, Learning and Control with Probabilistic Movement Primitives.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

Parisi, Simone (2020):
Reinforcement Learning with Sparse and Multiple Rewards.
Darmstadt, Technische Universität,
DOI: 10.25534/tuprints-00011372,
[Ph.D. Thesis]

Parisi, Simone ; Tateo, Davide ; Hensel, Maximilian ; D’Eramo, Carlo ; Peters, Jan ; Pajarinen, Joni (2022):
Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning. (Publisher's Version)
In: Algorithms, 15 (3), MDPI, e-ISSN 1999-4893,
DOI: 10.26083/tuprints-00021017,
[Article]

Ploeger, Kai ; Lutter, Michael ; Peters, Jan (2022):
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards. (Publisher's Version)
In: Proceedings of Machine Learning Research, 155, In: Proceedings of the 2020 Conference on Robot Learning, pp. 642-653,
Darmstadt, PMLR, Conference on Robot Learning (CoRL) 2020, Cambridge MA, USA, 16.-18.11.2020, DOI: 10.26083/tuprints-00020583,
[Conference or Workshop Item]

R

Rawal, Niyati ; Koert, Dorothea ; Turan, Cigdem ; Kersting, Kristian ; Peters, Jan ; Stock-Homburg, Ruth (2022):
ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition. (Publisher's Version)
In: Frontiers in Robotics and AI, 8, Frontiers Media S.A., e-ISSN 2296-9144,
DOI: 10.26083/tuprints-00020336,
[Article]

S

Schultheis, Matthias ; Belousov, Boris ; Abdulsamad, Hany ; Peters, Jan (2022):
Receding Horizon Curiosity. (Publisher's Version)
In: Proceedings of Machine Learning Research, 100, pp. 1278-1288, Darmstadt, PMLR, 3rd Conference on Robot Learning (CoRL 2019), Osaka, Japan, 30.10.- 1.11.2019, DOI: 10.26083/tuprints-00020578,
[Conference or Workshop Item]

Sousa Ewerton, Marco Antonio (2020):
Bidirectional Human-Robot Learning: Imitation and Skill Improvement.
Darmstadt, Technische Universität,
DOI: 10.25534/tuprints-00011875,
[Ph.D. Thesis]

T

Tanneberg, Daniel (2020):
Understand-Compute-Adapt: Neural Networks for Intelligent Agents. (Publisher's Version)
Darmstadt, Technische Universität Darmstadt,
DOI: 10.25534/tuprints-00017234,
[Ph.D. Thesis]

Tanneberg, Daniel ; Peters, Jan ; Rueckert, Elmar (2022):
Intrinsic motivation and mental replay enable efficient online adaptation in stochastic recurrent networks. (Postprint)
In: Neural Networks, 109, pp. 67-80. Elsevier, ISSN 0893-6080,
DOI: 10.26083/tuprints-00020537,
[Article]

Tanneberg, Daniel ; Peters, Jan ; Rueckert, Elmar (2022):
Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals. (Publisher's Version)
In: Proceedings of Machine Learning Research, 78, In: Proceedings of the 1st Annual Conference on Robot Learning, pp. 167-174,
Darmstadt, PMLR, CoRL2017 - Conference on Robot Learning 2017, Mountain View, California, 13.-15.11.2017, DOI: 10.26083/tuprints-00020580,
[Conference or Workshop Item]

Tanneberg, Daniel ; Ploeger, Kai ; Rueckert, Elmar ; Peters, Jan (2022):
SKID RAW: Skill Discovery From Raw Trajectories. (Postprint)
In: IEEE Robotics and Automation Letters, 6 (3), pp. 4696-4703. IEEE, ISSN 2377-3774, e-ISSN 2377-3766,
DOI: 10.26083/tuprints-00020536,
[Article]

Tosatto, Samuele (2021):
Off-Policy Reinforcement Learning for Robotics. (Publisher's Version)
Darmstadt, Technische Universität,
DOI: 10.26083/tuprints-00017536,
[Ph.D. Thesis]

Trick, Susanne ; Koert, Dorothea ; Peters, Jan ; Rothkopf, Constantin A. (2022):
Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction. (Postprint)
In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7009-7016,
Darmstadt, IEEE, International Conference on Intelligent Robots and Systems (IROS), Macau, China, 03.-08.11.2019, e-ISSN 2153-0866, ISBN 978-1-7281-4004-9,
DOI: 10.26083/tuprints-00020552,
[Conference or Workshop Item]

V

Vinogradska, Julia (2018):
Gaussian Processes in Reinforcement Learning: Stability Analysis and Efficient Value Propagation.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

van Hoof, Herke (2016):
Machine Learning through Exploration for Perception-Driven Robotics.
Darmstadt, Technische Universität Darmstadt,
[Ph.D. Thesis]

W

Wang, Zhikun (2013):
Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models.
Darmstadt, Technische Universität,
[Ph.D. Thesis]