Publications ordered by Year

The majority of the publications can also be obtained using Google Scholar where incomplete lists of citations are also given. The publication list sorted by type can be found using this link. A bibtex file with all our publications is also available.

submitted

•       Bib
  Klink, P.; Wolf, F.; Ploeger, K.; Peter, J.; Pajarinen, J. (submitted). Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning, Submitted to the IEEE Transactions on Robotics (T-Ro).
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  Liu, P.; Bou-Ammar H.; Peters, J.; Tateo D. (submitted). Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications, Submitted to the IEEE Transactions on Robotics (T-Ro).
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  Look, A.; Rakitsch, B.; Kandemir, M.; Peters, J. (submitted). Sampling-Free Probabilistic Deep State-Space Models, Submitted to Transactions on Pattern Analysis and Machine Intelligence (PAMI).
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  Lutter, M.; Silberbauer, J.; Watson, J.; Peters, J. (submitted). A Differentiable Newton-Euler Algorithm for Real-World Robotics, Submitted to the IEEE Transaction of Robotics (T-Ro).
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  Prasad, V.; Heitlinger, L; Koert, D.; Stock-Homburg, R.; Peters, J.; Chalvatzaki, G. (submitted). Learning Multimodal Latent Dynamics for Human-Robot Interaction, Submitted to the IEEE Transaction of Robotics (T-RO).

in press

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  Dam, T.; D'Eramo, C.; Peters, J.; Pajarinen, J. (in press). A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search, Journal of Artificial Intelligence Research (JAIR).
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  Gu, S.; Liu, P.; Kshirsagar, A.; Chen, G.; Peters, J.; Knoll, A. (in press). ROSCOM: Robust Safe Reinforcement Learning on Stochastic Constraint Manifold, IEEE Transactions on Automation Science and Engineering.
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  Klink, P.; D'Eramo, C.; Peters, J.; Pajarinen, J. (in press). On the Benefit of Optimal Transport for Curriculum Reinforcement Learning, IEEE Transaction on Pattern Analysis and Machine Intelligence (PAMI).

2024

•     Bib
  Abdulsamad, H.; Nickl, P.; Klink, P.; Peters, J. (2024). Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 46, 4, pp.1950-1963.
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  Al-Hafez, F.; Zhao, G.; Peters, J.; Tateo, D. (2024). Time-Efficient Reinforcement Learning with Stochastic Stateful Policies, International Conference on Learning Representations (ICLR).
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  Becker, N.; Gattung, E.; Hansel, K.; Schneider, T.; Zhu, Y.; Hasegawa, Y.; Peters, J. (2024). Integrating Visuo-tactile Sensing with Haptic Feedback for Teleoperated Robot Manipulation, IEEE ICRA 2024 Workshop on Robot Embodiment through Visuo-Tactile Perception.
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  Bhatt, A.; Palenicek, D.; Belousov, B.; Argus, M.; Amiranashvili, A.; Brox, T.; Peters, J. (2024). CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity, International Conference on Learning Representations (ICLR), Spotlight.
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  Boehm, A.; Schneider, T.; Belousov, B.; Kshirsagar, A.; Lin, L.; Doerschner, K.; Drewing, K.; Rothkopf, C.A.; Peters, J. (2024). What Matters for Active Texture Recognition With Vision-Based Tactile Sensors, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
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  Bohlinger, N.; Czechmanowski, G.; Krupka, M.; Kicki, P.; Walas, K.; Peters, J.; Tateo, D. (2024). One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion, Conference on Robot Learning (CoRL).
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  Derstroff, C.; Brugger, J.; Cerrato, M.; Peters, J.; Kramer, S. (2024). Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations, Proceedings of the National Conference on Artificial Intelligence (AAAI).
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  Drolet, M.; Stepputtis, S.; Kailas, S.; Jain, A.; Schaal, S.; Peters, J.; Ben Amor, H. (2024). A Comparison of Imitation Learning Algorithms for Bimanual Manipulation, IEEE Robotics and Automation Letters (RA-L).
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  Funk, N.; Helmut, E.; Chalvatzaki, G.; Calandra, R.; Peters, J. (2024). Evetac: An Event-based Optical Tactile Sensor for Robotic Manipulation, IEEE Transactions on Robotics (T-RO), 40, pp.3812-3832.
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  Geiss, H.J.; Al-Hafez, F.; Seyfarth, A.; Peters, J.; Tateo, D. (2024). Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid Locomotion, IEEE-RAS International Conference on Humanoid Robots (Humanoids).
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  Goeksu, Y.; Almeida-Correia, A.; Prasad, V.; Kshirsagar, A.; Koert, D.; Peters, J.; Chalvatzaki, G. (2024). Kinematically Constrained Human-like Bimanual Robot-to-Human Handovers, ACM/IEEE International Conference on Human Robot Interaction (HRI), Late Breaking Report.
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  Hahne, F.; Prasad V.; Kshirsagar A.; Koert D.; Stock-Homburg R. M.; Peters J.; Chalvatzaki G. (2024). Transition State Clustering for Interaction Segmentation and Learning, ACM/IEEE International Conference on Human Robot Interaction (HRI), Late Breaking Report.
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  Hendawy, A.; Peters, J.; D'Eramo, C. (2024). Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts, International Conference on Learning Representations (ICLR).
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  Herrmann, F.; Zach, S.B.; Banfi, J.; Peters, J.; Chalvatzaki, G.; Tateo, D. (2024). Safe and Efficient Path Planning under Uncertainty via Deep Collision Probability Fields, IEEE Robotics and Automation Letters (RA-L).
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  Holzmann, P.; Maik Pfefferkorn, M.; Peters, J.; Findeisen, R. (2024). Learning Energy-Efficient Trajectory Planning for Robotic Manipulators using Bayesian Optimization, Proceedings of the European Control Conference (ECC).
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  Jansonnie, P.; Wu, B.; Perez, J.; Peters J. (2024). Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation, IEEE-RAS International Conference on Humanoid Robots (Humanoids).
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  Kicki, P.; Liu, P.; Tateo, D.; Bou Ammar, H.; Walas, K.; Skrzypczynski, P.; Peters, J. (2024). Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks, IEEE Transactions on Robotics (T-Ro), and Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 40, pp.277-297.
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  Lin, L.; Boehm, A.; Belousov, B.; Kshirsagar, A.; Schneider, T.; Peters, J.; Doerschner, K.; Drewing, K. (2024). Task-Adapted Single-Finger Explorations of Complex Objects, Eurohaptics.
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  Nguyen, D.H.M.*; Le, A.T.*; Nguyen, T.Q.; Nghiem, T.D.; Duong-Tran, D. ; Peters, J.; Li, S.; Niepert, M.; Sonntag, D. (2024). Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model, Asian Conference on Machine Learning (ACML).
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  Nguyen, D.M.H.; Lukashina, N.; Nguyen, N.; Le, A.T.; Nguyen, T.T.; Ho, N.; Peters, J.; Sonntag, D.; Zaverkin, V.; Niepert, M. (2024). Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks, Proceedings of the International Conference on Machine Learning (ICML).
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  Palenicek, D.; Gruner, T.; Schneider, T.; Böhm, A.; Lenz, J.; Pfenning, I. and Krämer, E.; Peters, J. (2024). Learning Tactile Insertion in the Real World, IEEE ICRA 2024 Workshop on Robot Embodiment through Visuo-Tactile Perception.
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  Prasad, V.; Kshirsagar, A; Koert, D.; Stock-Homburg, R.; Peters, J.; Chalvatzaki, G. (2024). MoVEInt: Mixture of Variational Experts for Learning Human-Robot Interactions from Demonstrations, IEEE Robotics and Automation Letters (RA-L), 9, 7, pp.6043--6050.
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  Reddi, A.; Toelle, M.; Peters, J.; Chalvatzaki, G.; D'Eramo, C. (2024). Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula, International Conference on Learning Representations (ICLR), Spotlight.
•     Bib
  Scherf, L.; Gasche, L. A.; Chemangui, E.; Koert, D. (2024). Are You Sure? - Multi-Modal Human Decision Uncertainty Detection in Human-Robot Interaction, 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24).
•   Bib
  Spartakov, R.; Kshirsagar, A.; Mühl, D.; Schween, R.; Endres, D.M.; Bremmer, F.; Melzig, C.; Peters, J. (2024). Balancing on the Edge: Review and Computational Framework on the Dynamics of Fear of Falling and Fear of Heights in Postural Control, Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci).
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  Tiboni, G.; Klink, P.; Peters, J.; Tommasi, T.; D'Eramo, C.; Chalvatzaki, G. (2024). Domain Randomization via Entropy Maximization, International Conference on Learning Representations (ICLR).
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  Vincent, T.; Metelli, A.; Belousov, B.; Peters, J.; Restelli, M.; D'Eramo, C. (2024). Parameterized Projected Bellman Operator, Proceedings of the National Conference on Artificial Intelligence (AAAI).
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  Vincent, T.; Wahren, F.; Peters, J.; Belousov, B.; D'Eramo, C.; (2024). Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning, European Workshop on Reinforcement Learning (EWRL).
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  Vincent, T.; Wahren, F.; Peters, J.; Belousov, B.; D'Eramo, C.; (2024). Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning, ICML Workshop on Automated Reinforcement Learning.
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  Weng, Y.; Chun, S.; Ohashi, M.; Matsuda, T.; Sekimoria, Y.; Pajarinen, J.; Peters, J.; Maki, T. (2024). Autonomous Underwater Vehicle Link Alignment Control in Unknown Environments Using Reinforcement Learning, Journal of Field Robotics, 41, 6, pp.1724--1743.
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  Wiebe, F.; Turcato, N.; Dalla Libera, A.; Zhang, C.; Vincent, T.; Vyas, S.; Giacomuzzo, G.; Carli, R.; Romeres, D.; Sathuluri, A.; Zimmermann, M.; Belousov, B.; Peters, J.; Kirchner, F.; Kumar, S. (2024). Reinforcement Learning for Athletic Intelligence: Lessons from the 1st “AI Olympics with RealAIGym” Competition, The 33rd International Joint Conference on Artificial Intelligence.

2023

•     Bib
  Abdulsamad, H.; Peters, J. (2023). Model-Based Reinforcement Learning via Stochastic Hybrid Models, IEEE Open Journal of Control Systems, Special Section: Intersection of Machine Learning with Control, 2, pp.155 - 170, IEEE.
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  Al-Hafez, F.; Zhao, G.; Peters, J.; Tateo, D. (2023). LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion, Robot Learning Workshop, Conference on Neural Information Processing Systems (NeurIPS).
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  Al-Hafez, F.; Tateo, D.; Arenz, O.; Zhao, G.; Peters, J. (2023). LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning, International Conference on Learning Representations (ICLR).
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  Al-Hafez, F.; Tateo, D.; Arenz, O.; Zhao, G.; Peters, J. (2023). Least Squares Inverse Q-Learning, European Workshop on Reinforcement Learning (EWRL).
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  Arenz, O.; Dahlinger, P.; Ye, Z.; Volpp, M.; Neumann, G. (2023). A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models, Transactions on Machine Learning Research (TMLR).
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  Bethge, J.; Pfefferkorn, M.; Rose, A.; Peters, J.; Findeisen, R. (2023). Model predictive control with Gaussian-process-supported dynamical constraints for autonomous vehicles, Proceedings of the 22nd World Congress of the International Federation of Automatic Control.
•     Bib
  Bjelonic, F.; Lee, J.; Arm, P.; Sako, D.; Tateo, D.; Peters, J.; Hutter, M. (2023). Learning-Based Design and Control for Quadrupedal Robots With Parallel-Elastic Actuators, IEEE Robotics and Automation Letters (R-AL), 8, 3, pp.1611-1618.
•     Bib
  Boehm, A.; Schneider, T.; Belousov, B.; Kshirsagar, A.; Lin, L.; Doerschner, K.; Drewing, K.; Rothkopf, C.A.; Peters, J. (2023). Tactile Active Texture Recognition With Vision-Based Tactile Sensors, NeurIPS Workshop on Touch Processing: a new Sensing Modality for AI.
•     Bib
  Buechler, D.; Calandra, R.; Peters, J. (2023). Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots, Robotics and Autonomous Systems, 159, 104230.
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  Buechler, D.; Guist, S.; Calandra, R.; Berenz, V.; Schoelkopf, B.; Peters, J. (2023). Learning to Play Table Tennis From Scratch using Muscular Robots, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE T-TRo Track.
•       Bib
  Carvalho, J.; Le, A. T.; Baierl, M.; Koert, D.; Peters, J. (2023). Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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  Chen, Q.; Zhu, Y.; Hansel, Kay.; Aoyama, T.; Hasegawa, Y. (2023). Human Preferences and Robot Constraints Aware Shared Control for Smooth Follower Motion Execution, IEEE International Symposium on Micro-NanoMechatronics and Human Science (MHS), IEEE.
•     Bib
  Flynn, H.; Reeb, D.; Kandemir, M.; Peters, J. (2023). PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 45, 12, pp.15308-15327.
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  Flynn, H.; Reeb, D.; Kandemir, M.; Peters, J. (2023). Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
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  Funk, N.; Mueller, P.-O.; Belousov, B.; Savchenko, A.; Findeisen, R.; Peters, J. (2023). High-Resolution Pixelwise Contact Area and Normal Force Estimation for the GelSight Mini Visuotactile Sensor Using Neural Networks, Embracing Contacts-Workshop at ICRA 2023.
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  Gruner, T.; Belousov, B.; Muratore, F.; Palenicek, D.; Peters, J. (2023). Pseudo-Likelihood Inference, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
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  Gu, S.; Kshirsagar, A.; Du Y.; Chen G.; Peters J.; Knoll A. (2023). A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors, Frontiers in Neurorobotics, 17, 1280341.
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  Hansel, K.; Urain, J.; Peters, J.; Chalvatzaki, G. (2023). Hierarchical Policy Blending as Inference for Reactive Robot Control, 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE.
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  Ju, S.; van Vliet, P.; Arenz, O.; Peters, J. (2023). Digital Twin of a Driver-in-the-Loop Race Car Simulation with Contextual Reinforcement Learning, IEEE Robotics and Automation Letters (RA-L), 8, 7, pp.4107-4114.
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  Lach, L.; Haschke, R.; Tateo, D.; Peters, J.; Ritter, H.; Sol, J.; Torras, C. (2023). Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot, NeurIPS 2023 Workshop on Touch Processing.
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  Le, A. T.; Chalvatzaki, G.; Biess, A.; Peters, J. (2023). Accelerating Motion Planning via Optimal Transport, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
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  Le, A. T.; Chalvatzaki, G.; Biess, A.; Peters, J. (2023). Accelerating Motion Planning via Optimal Transport, IROS 2023 Workshop on Differentiable Probabilistic Robotics: Emerging Perspectives on Robot Learning, [Oral].
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  Le, A. T.; Chalvatzaki, G.; Biess, A.; Peters, J. (2023). Accelerating Motion Planning via Optimal Transport, NeurIPS 2023 Workshop Optimal Transport and Machine Learning, [Oral].
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  Le, A. T.; Hansel, K.; Peters, J.; Chalvatzaki, G. (2023). Hierarchical Policy Blending As Optimal Transport, 5th Annual Learning for Dynamics & Control Conference (L4DC), PMLR.
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  Liu, P.; Zhang, K.; Tateo, D.; Jauhri, S.; Hu, Z.; Peters, J. Chalvatzaki, G. (2023). Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction, 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE.
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  Liu, Y.; Belousov, B.; Funk, N.; Chalvatzaki, G.; Peters, J.; Tessman, O. (2023). Auto(mated)nomous Assembly, International Conference on Trends on Construction in the Post-Digital Era, pp.167-181, Springer, Cham.
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  Loeckel, S.; Ju, S.; Schaller, M.; van Vliet, P..; Peters, J. (2023). An Adaptive Human Driver Model for Realistic Race Car Simulations, IEEE Transactions on Systems, Man and Cybernetics: Systems (TSMC), 53, 11, pp.6718-6730.
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  Look, A.; Kandemir, M.; Rakitsch, B.; Peters, J. (2023). A Deterministic Approximation to Neural SDEs, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 45, 4, pp.4023-4037.
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  Look, A.; Kandemir, M.; Rakitsch, B.; Peters, J. (2023). Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems, Transactions on Machine Learning Research (TMLR).
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  Luis, C.; Bottero, A.G.; Vinogradska, J.; Berkenkamp, F.; Peters, J. (2023). Model-Based Uncertainty in Value Functions, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).
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  Lutter, M.; Belousov, B.; Mannor, S.; Fox, D.; Garg, A.; Peters, J. (2023). Continuous-Time Fitted Value Iteration for Robust Policies, IEEE Transaction on Pattern Analysis and Machine Intelligence (PAMI).
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  Lutter, M.; Peters, J. (2023). Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models, International Journal of Robotics Research (IJRR), 42, 3.
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  Metternich, H.; Hendawy, A.; Klink, P.; Peters, J.; D'Eramo, C. (2023). Using Proto-Value Functions for Curriculum Generation in Goal-Conditioned RL, NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning.
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  Mittenbuehler, M.; Hendawy, A.; D'Eramo, C.; Chalvatzaki, G. (2023). Parameter-efficient Tuning of Pretrained Visual-Language Models in Multitask Robot Learning, CoRL 2023 Workshop on Learning Effective Abstractions for Planning (LEAP).
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  Palenicek, D.; Lutter, M.; Carvalho, J.; Peters, J. (2023). Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning, International Conference on Learning Representations (ICLR).
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  Peters, S.; Peters, J.; Findeisen, R. (2023). Quantifying Uncertainties along the Automated Driving Stack, ATZ worldwide volume, 125, pp.62-65.
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  Rother, D. (2023). Implicitly Cooperative Agents through Impact-Aware Learning, European Conference on Artificial Intelligence (ECAI).
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  Rother, D.; Weisswange, T.H.; Peters, J. (2023). Disentangling Interaction using Maximum Entropy Reinforcement Learning in Multi-Agent Systems, European Conference on Artificial Intelligence (ECAI).
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  Scherf, L.; Schmidt, A.; Pal, S.; Koert, D. (2023). Interactively learning behavior trees from imperfect human demonstrations, Frontiers in Robotics and AI, 10.
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  Toelle, M.; Belousov, B.; Peters, J. (2023). A Unifying Perspective on Language-Based Task Representations for Robot Control, CoRL Workshop on Language and Robot Learning: Language as Grounding.
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  Urain, J.; Funk, N.; Peters, J.; Chalvatzaki G (2023). SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion, International Conference on Robotics and Automation (ICRA).
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  Urain, J.; Li, A.; Liu, P.; D'Eramo, C.; Peters, J. (2023). Composable energy policies for reactive motion generation and reinforcement learning, International Journal of Robotics Research (IJRR).
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  Urain, J.; Tateo, D.; Peters, J. (2023). Learning Stable Vector Fields on Lie Groups, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE R-AL Track.
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  Vincent, T.; Belousov, B.; D'Eramo, C.; Peters, J. (2023). Iterated Deep Q-Network: Efficient Learning of Bellman Iterations for Deep Reinforcement Learning, European Workshop on Reinforcement Learning (EWRL).
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  Vincent, T.; Metelli, A.; Peters, J.; Restelli, M.; D'Eramo, C. (2023). Parameterized projected Bellman operator, ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems.
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  Watson, J.; Peters, J.; (2023). Sample-Efficient Online Imitation Learning using Pretrained Behavioural Cloning Policies, NeurIPS 6th Robot Learning Workshop: Pretraining, Fine-Tuning, and Generalization with Large Scale Models.
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  Zelch, C.; Peters, J.; von Stryk, C. (2023). Start State Selection for Control Policy Learning from Optimal Trajectories, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
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  Zelch, C.; Peters, J.; von Stryk, O. (2023). Clustering of Motion Trajectories by a Distance Measure Based on Semantic Features, Proceedings of the IEEE International Conference on Humanoid Robots (Humanoids).
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  Zhu, Y.; Nazirjonov, S.; Jiang, B.; Colan, J.; Aoyama, T.; Hasegawa, Y.; Belousov, B.; Hansel, K.; Peters, J. (2023). Visual Tactile Sensor Based Force Estimation for Position-Force Teleoperation, IEEE International Conference on Cyborg and Bionic Systems (CBS), pp.49-52.

2022

•     Bib
  Akrour, R.; Tateo, D.; Peters, J. (2022). Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 44, 10, pp.6795-6806.
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  Belousov, B.; Wibranek, B.; Schneider, J.; Schneider, T.; Chalvatzaki, G.; Peters, J.; Tessmann, O. (2022). Robotic Architectural Assembly with Tactile Skills: Simulation and Optimization, Automation in Construction, 133, pp.104006.
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  Bottero, A.G.; Luis, C.E.; Vinogradska, J.; Berkenkamp, F.; Peters, J. (2022). Information-Theoretic Safe Exploration with Gaussian Processes, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
•     Bib
  Buechler, D.; Guist, S.; Calandra, R.; Berenz, V.; Schoelkopf, B.; Peters, J. (2022). Learning to Play Table Tennis From Scratch using Muscular Robots, IEEE Transactions on Robotics (T-Ro), 38, 6, pp.3850-3860.
•     Bib
  Carvalho, J.; Baierl, M; Urain, J; Peters, J. (2022). Conditioned Score-Based Models for Learning Collision-Free Trajectory Generation, NeurIPS 2022 Workshop on Score-Based Methods.
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  Carvalho, J.; Koert, D.; Daniv, M.; Peters, J. (2022). Adapting Object-Centric Probabilistic Movement Primitives with Residual Reinforcement Learning, 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids).
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  Carvalho, J.; Peters, J. (2022). An Analysis of Measure-Valued Derivatives for Policy Gradients, Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM).
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  Cowen-Rivers, A.; Lyu, W.; Tutunov, R.; Wang, Z.; Grosnit, A.; Griffiths, R.R.; Maraval, A.; Jianye, H.; Wang, J.; Peters, J.; Bou Ammar, H. (2022). HEBO: An Empirical Study of Assumptions in Bayesian Optimisation, Journal of Artificial Intelligence Research, 74, pp.1269-1349.
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  Cowen-Rivers, A.I.; Palenicek, D.; Moens, V.; Abdullah, M.A.; Sootla, A.; Wang, J.; Bou-Ammar, H. (2022). SAMBA: safe model-based & active reinforcement learning, Machine Learning.
•     Bib
  Dam, T.; Chalvatzaki, G.; Peters, J.; Pajarinen, J. (2022). Monte-carlo robot path planning, IEEE Robotics and Automation Letters (RA-L), 7, 4, pp.11213-11220.
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  Flynn, H.; Reeb, D.; Kandemir, M.; Peters, J. (2022). PAC-Bayesian Lifelong Learning For Multi-Armed Bandits, Data Mining and Knowledge Discovery, 36, 2, pp.841-876.
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  Funk, N.; Menzenbach, S.; Chalvatzaki, G.; Peters, J. (2022). Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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  Funk, N.; Schaff, C.; Madan, R.; Yoneda, T.; Urain, J.; Watson, J.; Gordon, E.; Widmaier, F; Bauer, S.; Srinivasa, S.; Bhattacharjee, T.; Walter, M.; Peters, J. (2022). Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation, IEEE Robotics and Automation Letters (R-AL).
•     Bib
  Galljamov, R.; Zhao, G.; Belousov, B.; Seyfarth, A.; Peters, J. (2022). Improving Sample Efficiency of Example-Guided Deep Reinforcement Learning for Bipedal Walking, 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids).
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  Hansel, K.; Moos, J.; Abdulsamad, H.; Stark, S.; Clever, D.; Peters, J. (2022). Robust Reinforcement Learning: A Review of Foundations and Recent Advances, Machine Learning and Knowledge Extraction (MAKE), 4, 1, pp.276--315, MDPI.
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  Klink, P.; D`Eramo, C.; Peters, J.; Pajarinen, J. (2022). Boosted Curriculum Reinforcement Learning, International Conference on Learning Representations (ICLR).
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  Klink, P.; Yang, H.; D'Eramo, C.; Peters, J.; Pajarinen, J. (2022). Curriculum Reinforcement Learning via Constrained Optimal Transport, International Conference on Machine Learning (ICML).
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  Le, A. T.; Urain, J.; Lambert, A.; Chalvatzaki, G.; Boots, B.; Peters, J. (2022). Learning Implicit Priors for Motion Optimization, RSS 2022 Workshop on Implicit Representations for Robotic Manipulation.
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  Liu, P.; Zhang, K.; Tateo, D.; Jauhri, S.; Peters, J.; Chalvatzaki, G.; (2022). Regularized Deep Signed Distance Fields for Reactive Motion Generation, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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  Loeckel, S.; Kretschi, A.; van Vliet, P.; Peters, J. (2022). Identification and modelling of race driving styles, Vehicle System Dynamics, 60, 8, pp.2890--2918.
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  Memmel, M.; Liu, P.; Tateo, D.; Peters, J. (2022). Dimensionality Reduction and Prioritized Exploration for Policy Search, 25th International Conference on Artificial Intelligence and Statistics (AISTATS).
•       Bib
  Muratore, F.; Ramos, F.; Turk, G.; Yu, W.; Gienger, M.; Peters, J. (2022). Robot Learning from Randomized Simulations: A Review, Frontiers in Robotics and AI, 9.
•     Bib
  Palenicek, D.; Lutter, M., Peters, J. (2022). Revisiting Model-based Value Expansion, Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM).
•     Bib
  Parisi, S.; Tateo, D.; Hensel, M.; D'Eramo, C.; Peters, J.; Pajarinen, J. (2022). Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning, Algorithms, 15, 3, pp.81.
•     Bib
  Ploeger, K.; Peters, J. (2022). Controlling the Cascade: Kinematic Planning for N-ball Toss Juggling, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Prasad, V.; Koert, D.; Stock-Homburg, R.; Peters, J.; Chalvatzaki, G. (2022). MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction, IEEE-RAS International Conference on Humanoid Robots (Humanoids).
•       Bib
  Prasad, V.; Stock-Homburg, R.; Peters, J. (2022). Human-Robot Handshaking: A Review, International Journal of Social Robotics (IJSR), 14, 1, pp.277-293.
•     Bib
  Scherf, L.; Turan, C.; Koert, D. (2022). Learning from Unreliable Human Action Advice in Interactive Reinforcement Learning, 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids).
•       Bib
  Schneider, T.; Belousov, B.; Abdulsamad, H.; Peters, J. (2022). Active Inference for Robotic Manipulation, 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM).
•       Bib
  Schneider, T.; Belousov, B.; Chalvatzaki, G.; Romeres, D.; Jha, D.K.; Peters, J. (2022). Active Exploration for Robotic Manipulation, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•       Bib
  Siebenborn, M.; Belousov, B.; Huang, J.; Peters, J. (2022). How Crucial is Transformer in Decision Transformer?, Foundation Models for Decision Making Workshop at Neural Information Processing Systems.
•     Bib
  Tosatto, S.; Carvalho, J.; Peters, J. (2022). Batch Reinforcement Learning with a Nonparametric Off-Policy Policy Gradient, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 44, 10, pp.5996--6010.
•       Bib
  Urain, J.*; Le, A.T.*; Lambert, A.*; Chalvatzaki, G.; Boots, B.; Peters, J. (2022). Learning Implicit Priors for Motion Optimization, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Urain, J.; Tateo, D; Peters, J. (2022). Learning Stable Vector Fields on Lie Groups, Robotics and Automation Letters (RA-L).
•     Bib
  Vorndamme, J.; Carvalho, J.; Laha, R.; Koert, D.; Figueredo, L.; Peters, J.; Haddadin, S. (2022). Integrated Bi-Manual Motion Generation and Control shaped for Probabilistic Movement Primitives, 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids).
•   Bib
  Watson, J.; Hanher, B.; Peters, J. (2022). Differentiable Simulators as Gaussian Processes, R:SS Workshop: Differentiable Simulation for Robotics.
•     Bib
  Watson, J.; Peters, J. (2022). Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes, Conference on Robot Learning (CoRL).
•   Bib
  Watson, J.; Peters, J.; (2022). Stationary Posterior Policy Iteration with Variational Inference, The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM).
•     Bib
  Weng, Y.; Matsuda, T.; Sekimoria, Y.; Pajarinen, J.; Peters, J.; Maki, T. (2022). Establishment of Line-of-Sight Optical Links Between Autonomous Underwater Vehicles: Field Experiment and Performance Validation, Applied Ocean Research, 129.
•     Bib
  Weng, Y.; Matsuda, T.; Sekimuri, Y.; Pajarinen, J.; Peters, J.; Maki, T. (2022). Pointing Error Control of Underwater Wireless Optical Communication on Mobile Platform, IEEE Photonics Technology Letters, 34, 13, pp.699-702.
•     Bib
  Weng, Y.; Pajarinen, J.; Akrour, R.; Matsuda, T.; Peters, J.; Maki, T. (2022). Reinforcement Learning Based Underwater Wireless Optical Communication Alignment for Multiple Autonomous Underwater Vehicles, IEEE Journal of Oceanic Engineering, 47, 4, pp.1231-1245.
•       Bib
  You, B.; Arenz, O.; Chen, Y.; Peters, J. (2022). Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from Images, Neurocomputing.
•     Bib
  Zheng, Y.; Veiga, F.F.; Peters, J.; Santos, V.J. (2022). Autonomous Learning of Page Flipping Movements via Tactile Feedback, IEEE Transactions on Robotics (T-Ro), 38, 5, pp.2734 - 2749.
•     Bib
  Zheng, Y.; Veiga, F.F.; Peters, J.; Santos, V.J. (2022). Autonomous Learning of Page Flipping Movements via Tactile Feedback, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

2021

•     Bib
  Abdulsamad, H.; Dorau, T.; Belousov, B.; Zhu, J.-J; Peters, J. (2021). Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative Entropy Trust-Regions, arXiv.
•     Bib
  Abdulsamad, H.; Nickl, P.; Klink, P.; Peters, J. (2021). A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Abdulsamad, H.; Peters, J. (2021). Model-Based Reinforcement Learning for Stochastic Hybrid Systems, arXiv.
•     Bib
  Akrour, R.; Atamna, A.; Peters, J. (2021). Convex Optimization with an Interpolation-based Projection and its Application to Deep Learning, Machine Learning (MACH), 110, 8, pp.2267-2289.
•     Bib
  Bauer, S.; Wüthrich, W.; Widmaier, F.; Buchholz, A.; Stark, S.; Goyal, A.; Steinbrenner, T.; Akpo, J.; Joshi, S.; Berenz, V.; Agrawal, V.; Funk, N.; Urain, J.; Peters, J.; Watson, J.; Et, A.L.l (2021). Real Robot Challenge: A Robotics Competition in the Cloud, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
•     Bib
  Belousov, B.; Abdulsamad H.; Klink, P.; Parisi, S.; Peters, J. (2021). Reinforcement Learning Algorithms: Analysis and Applications, Studies in Computational Intelligence, Springer International Publishing.
•     Bib
  Bustamante, S.; Peters, J.; Schoelkopf, B.; Grosse-Wentrup, M.; Jayaram, V. (2021). ArmSym: a virtual human-robot interaction laboratory for assistive robotics, IEEE Transactions on Human-Machine Systems, 51, 6, pp.568-577.
•     Bib
  Carvalho, J., Tateo, D., Muratore, F., Peters, J. (2021). An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients, International Joint Conference on Neural Networks (IJCNN).
•     Bib
  Dam, T.; D'Eramo, C.; Peters, J.; Pajarinen J. (2021). Convex Regularization in Monte-Carlo Tree Search, Proceedings of the International Conference on Machine Learning (ICML).
•     Bib
  D`Eramo, C.; Cini, A.; Nuara, A.; Pirotta, M.; Alippi, C.; Peters, J.; Restelli, M. (2021). Gaussian Approximation for Bias Reduction in Q-Learning, Journal of Machine Learning Research (JMLR).
•     Bib
  D`Eramo, C.; Tateo, D; Bonarini, A.; Restelli, M.; Peters, J. (2021). MushroomRL: Simplifying Reinforcement Learning Research, Journal of Machine Learning Research (JMLR), 22, 131, pp.1-5.
•       Bib
  Funk, N.; Baumann, D.; Berenz, V.; Trimpe, S. (2021). Learning event-triggered control from data through joint optimization, IFAC Journal of Systems and Control, 16, pp.100144.
•       Bib
  Funk, N.; Chalvatzaki, G.; Belousov, B.; Peters, J. (2021). Learn2Assemble with Structured Representations and Search for Robotic Architectural Construction, Conference on Robot Learning (CoRL).
•     Bib
  Hansel, K.; Moos, J.; Derstroff, C. (2021). Benchmarking the Natural Gradient in Policy Gradient Methods and Evolution Strategies, Reinforcement Learning Algorithms: Analysis and Applications, pp.69--84, Springer.
•     Bib
  Hoefer, S.; Bekris, K.; Handa, A.; Gamboa, J.C.; Golemo, F.; Mozifian, M.; Atkeson, C.G., Fox, D.; Goldberg, K.; Leonard, J.; Liu, C.K.; Peters, J.; Song, S.; Welinder, P.; White, M. (2021). Sim2Real in Robotics and Automation: Applications and Challenges, IEEE Transactions on Automation Science (T-ASE), 18, 2, pp.398-400.
•     Bib
  Klink, P.; Abdulsamad, H.; Belousov, B.; D'Eramo, C.; Peters, J.; Pajarinen, J. (2021). A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning, Journal of Machine Learning Research (JMLR).
•     Bib
  Knaust, M.; Koert, D. (2021). Guided Robot Skill Learning: A User-Study on Learning Probabilistic Movement Primitives with Non-Experts, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Lampariello, R.; Mishra, H.; Oumer, N.W.; Peters, J. (2021). Robust Motion Prediction of a Free-Tumbling Satellite with On-Ground Experimental Validation, Journal of Guidance, Control, and Dynamics, 44, 10, pp.1777-1793.
•     Bib
  Li, Q.; Chalvatzaki, G.; Peters, J.; Wang, Y. (2021). Directed Acyclic Graph Neural Network for Human Motion Prediction, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
•       Bib
  Liu, P.; Tateo, D.; Bou-Ammar, H.; Peters, J. (2021). Efficient and Reactive Planning for High Speed Robot Air Hockey, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Liu, P.; Tateo, D.; Bou-Ammar, H.; Peters, J. (2021). Robot Reinforcement Learning on the Constraint Manifold, Proceedings of the Conference on Robot Learning (CoRL).
•     Bib
  Lutter, M.; Clever, D.; Kirsten, R.; Listmann, K.; Peters, J. (2021). Building Skill Learning Systems for Robotics, Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE).
•     Bib
  Lutter, M.; Mannor, S.; Peters, J.; Fox, D.; Garg, A. (2021). Value Iteration in Continuous Actions, States and Time, International Conference on Machine Learning (ICML).
•     Bib
  Lutter, M.; Mannor, S.; Peters, J.; Fox, D.; Garg, A. (2021). Robust Value Iteration for Continuous Control Tasks, Robotics: Science and Systems (RSS).
•     Bib
  Lutter, M.; Silberbauer, J.; Watson, J.; Peters, J. (2021). Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Morgan, A.; Nandha, D.; Chalvatzaki, G.; D'Eramo, C.; Dollar, A.; Peters, J. (2021). Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
•       Bib
  Muratore, F.; Eilers, C.; Gienger, M.; Peters, J. (2021). Data-efficient Domain Randomization with Bayesian Optimization, IEEE Robotics and Automation Letters (RA-L), with Presentation at the IEEE International Conference on Robotics and Automation (ICRA), IEEE.
•       Bib
  Muratore, F.; Gienger, M.; Peters, J. (2021). Assessing Transferability from Simulation to Reality for Reinforcement Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 43, 4, pp.1172-1183, IEEE.
•     Bib
  Muratore, F.; Gruner, T.; Wiese, F.; Belousov, B.; Gienger, M.; Peters, J. (2021). Neural Posterior Domain Randomization, Conference on Robot Learning (CoRL).
•     Bib
  Palenicek, D. (2021). A Survey on Constraining Policy Updates Using the KL Divergence, Reinforcement Learning Algorithms: Analysis and Applications, pp.49-57.
•     Bib
  Prasad, V.; Stock-Homburg, R.; Peters, J. (2021). Learning Human-like Hand Reaching for Human-Robot Handshaking, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Rawal, N.; Koert, D.; Turan, C.; Kersting, K.; Peters, J.; Stock-Homburg, R. (2021). ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition, Frontiers in Robotics & AI, 8, 730317.
•     Bib
  Tanneberg, D.; Ploeger, K.; Rueckert, E.; Peters, J. (2021). SKID RAW: Skill Discovery from Raw Trajectories, IEEE Robotics and Automation Letters (RA-L).
•       Bib
  Tosatto, S.; Akrour, R.; Peters, J. (2021). An Upper Bound of the Bias of Nadaraya-Watson Kernel Regression under Lipschitz Assumptions, Stats, 4, pp.1--17.
•     Bib
  Tosatto, S.; Chalvatzaki, G.; Peters, J. (2021). Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Urain, J.; Li, A.; Liu, P.; D'Eramo, C.; Peters, J. (2021). Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning, Robotics: Science and Systems (RSS).
•     Bib
  Watson, J.; Lin J. A.; Klink, P.; Pajarinen, J.; Peters, J. (2021). Latent Derivative Bayesian Last Layer Networks, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).
•     Bib
  Watson, J.; Lin, J. A.; Klink, P.; Peters, J. (2021). Neural Linear Models with Functional Gaussian Process Priors, 3rd Symposium on Advances in Approximate Bayesian Inference (AABI).
•     Bib
  Watson, J.; Peters, J. (2021). Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk, American Control Conference (ACC).
•     Bib
  Wibranek, B.; Liu, Y.; Funk, N.; Belousov, B.; Peters, J.; Tessmann, O. (2021). Reinforcement Learning for Sequential Assembly of SL-Blocks: Self-Interlocking Combinatorial Design Based on Machine Learning, Proceedings of the 39th eCAADe Conference.

2020

•     Bib
  Abdulsamad, H.; Peters, J. (2020). Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation, 2nd Annual Conference on Learning for Dynamics and Control.
•   Bib
  Abdulsamad, H.; Peters, J. (2020). Learning Hybrid Dynamics and Control, ECML/PKDD Workshop on Deep Continuous-Discrete Machine Learning.
•     Bib
  Abi-Farraj, F.; Pacchierotti, C.; Arenz, O.; Neumann, G.; Giordano, P. (2020). Haptic-based Guided Grasping in a Cluttered Environment, IEEE Haptics Symposium.
•     Bib
  Agudelo-Espana, D.; Gomez-Gonzalez, S.; Bauer, S.; Schoelkopf, B.; Peters, J. (2020). Bayesian Online Prediction of Change Points, Conference on Uncertainty in Artificial Intelligence (UAI).
•     Bib
  Almeida Santos, A.; Gil, C.E.M.; Peters, J.; Steinke, F. (2020). Decentralized Data-Driven Tuning of Droop Frequency Controllers, 2020 IEEE PES Innovative Smart Grid Technologies Europe.
•     Bib
  Arenz, O.; Neumann, G. (2020). Non-Adversarial Imitation Learning and its Connections to Adversarial Methods, arXiv.
•     Bib
  Arenz, O.; Zhong, M.; Neumann G. (2020). Trust-Region Variational Inference with Gaussian Mixture Models, Journal of Machine Learning Research (JMLR).
•     Bib
  Becker, P.; Arenz, O.; Neumann, G. (2020). Expected Information Maximization: Using the I-Projection for Mixture Density Estimation, International Conference on Learning Representations (ICLR).
•       Bib
  Dam, T.; Klink, P.; D'Eramo, C.; Peters, J.; Pajarinen, J. (2020). Generalized Mean Estimation in Monte-Carlo Tree Search, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).
•     Bib
  D`Eramo, C.; Tateo, D.; Bonarini, A.; Restelli, M.; Peters, J. (2020). Sharing Knowledge in Multi-Task Deep Reinforcement Learning, International Conference in Learning Representations (ICLR).
•     Bib
  Eilers, C.; Eschmann, J.; Menzenbach, R.; Belousov, B.; Muratore, F.; Peters, J. (2020). Underactuated Waypoint Trajectory Optimization for Light Painting Photography, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Ewerton, M.; Arenz, O.; Peters, J. (2020). Assisted Teleoperation in Changing Environments with a Mixture of Virtual Guides, Advanced Robotics, 34.
•     Bib
  Gomez-Gonzalez, S.; Neumann, G.; Schoelkopf, B.; Peters, J. (2020). Adaptation and Robust Learning of Probabilistic Movement Primitives, IEEE Transactions on Robotics (T-Ro), 36, 2, pp.366-379.
•     Bib
  Gomez-Gonzalez, S.; Prokudin, S.; Schoelkopf, B.; Peters, J. (2020). Real Time Trajectory Prediction Using Deep Conditional Generative Models, IEEE Robotics and Automation Letters (ICRA/RA-L), with Presentation at the IEEE International Conference on Robotics and Automation (ICRA), 5, 2, pp.970-976.
•     Bib
  Keller, L.; Tanneberg, D.; Stark, S.; Peters, J. (2020). Model-Based Quality-Diversity Search for Efficient Robot Learning, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Klink, P.; D'Eramo, C.; Peters, J.; Pajarinen, J. (2020). Self-Paced Deep Reinforcement Learning, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
•       Bib
  Koert, D.; Kircher, M.; Salikutluk, V.; D'Eramo, C.; Peters, J. (2020). Multi-Channel Interactive Reinforcement Learning for Sequential Tasks, Frontiers in Robotics and AI Human-Robot Interaction.
•     Bib
  Koert, D.; Trick, S.; Ewerton, M.; Lutter, M.; Peters, J. (2020). Incremental Learning of an Open-Ended Collaborative Skill Library, International Journal of Humanoid Robotics (IJHR), 17, 1.
•     Bib
  Lauri, M.; Pajarinen, J.; Peters, J.; Frintrop, S. (2020). Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization, IEEE Robotics and Automation Letters (RA-L), 5, 4, pp.5323-5330.
•     Bib
  Laux, M.; Arenz, O.; Pajarinen, J.; Peters, J. (2020). Deep Adversarial Reinforcement Learning for Object Disentangling, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020).
•   Bib
  Lioutikov, R.; Maeda, G.; Veiga, F.F.; Kersting, K.; Peters, J. (2020). Learning Attribute Grammars for Movement Primitive Sequencing, International Journal of Robotics Research (IJRR), 39, 1, pp.21-38.
•     Bib
  Loeckel, S.; Peters, J.; van Vliet, P. (2020). A Probabilistic Framework for Imitating Human Race Driver Behavior, IEEE Robotics and Automation Letters (RA-L), with Presentation at the IEEE International Conference on Robotics and Automation (ICRA), 5, 2.
•   Bib
  Lutter, M.; Clever, D.; Belousov, B.; Listmann, K.; Peters, J. (2020). Evaluating the Robustness of HJB Optimal Feedback Control, International Symposium on Robotics.
•   Bib
  Lutter, M.; Silberbauer, J.; Watson, J.; Peters, J. (2020). A Differentiable Newton Euler Algorithm for Multi-body Model Learning, R:SS Structured Approaches to Robot Learning Workshop.
•     Bib
  Motokura, K.; Takahashi, M.; Ewerton, M.; Peters, J. (2020). Plucking Motions for Tea Harvesting Robots Using Probabilistic Movement Primitives, IEEE Robotics and Automation Letters (ICRA/RA-L), with Presentation at the IEEE International Conference on Robotics and Automation (ICRA), 5, 2, pp.2377-3766.
•     Bib
  Ploeger, K.; Lutter, M.; Peters, J. (2020). High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards, Conference on Robot Learning (CoRL).
•     Bib
  Prasad, V.; Stock-Homburg, R.; Peters, J. (2020). Advances in Human-Robot Handshaking, International Conference on Social Robotics, Springer.
•     Bib
  Rother, D., Haider, T., & Eger, S (2020). CMCE at SemEval-2020 Task 1: Clustering on Manifolds of Contextualized Embeddings to Detect Historical Meaning Shifts, 14th International Workshop on Semantic Evaluation (SemEval), pp.187-193.
•     Bib
  Stock-Homburg, R.; Peters, J.; Schneider, K.; Prasad, V.; Nukovic, L. (2020). Evaluation of the Handshake Turing Test for anthropomorphic Robots, Proceedings of the ACM/IEEE International Conference on Human Robot Interaction (HRI), Late Breaking Report.
•       Bib
  Tanneberg, D.; Rueckert, E.; Peters, J. (2020). Evolutionary Training and Abstraction Yields Algorithmic Generalization of Neural Computers, Nature Machine Intelligence, 2, 12, pp.753-763.
•     Bib
  Tosatto, S.; Carvalho, J.; Abdulsamad, H.; Peters, J. (2020). A Nonparametric Off-Policy Policy Gradient, Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS).
•     Bib
  Tosatto, S.; Stadtmueller, J.; Peters, J. (2020). Dimensionality Reduction of Movement Primitives in Parameter Space, arXiv.
•     Bib
  Urain, J.; Ginesi, M.; Tateo, D.; Peters, J. (2020). ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows, IEEE/RSJ International Conference on Intelligent Robots and Systems.
•     Bib
  Urain, J.; Tateo, D.; Ren, T.; Peters, J. (2020). Structured policy representation: Imposing stability in arbitrarily conditioned dynamic systems, NeurIPS 2020, 3rd Robot Learning Workshop, pp.7.
•     Bib
  Veiga, F. F.; Akrour, R.; Peters, J. (2020). Hierarchical Tactile-Based Control Decomposition of Dexterous In-Hand Manipulation Tasks, Frontiers in Robotics and AI.
•     Bib
  Veiga, F. F.; Edin B.B; Peters, J. (2020). Grip Stabilization through Independent Finger Tactile Feedback Control, Sensors (Special Issue on Sensors and Robot Control), 20.
•     Bib
  Vinogradska, J.; Bischoff, B.; Koller, T.; Achterhold, J.; Peters, J. (2020). Numerical Quadrature for Probabilistic Policy Search, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 42, 1, pp.164-175.
•   Bib
  Watson, J.; Imohiosen A.; Peters, J. (2020). Active Inference or Control as Inference? A Unifying View, International Workshop on Active Inference.
•     Bib
  Zelch, C.; Peters, J.; von Stryk, O. (2020). Learning Control Policies from Optimal Trajectories, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).

2019

•   Bib
  Abdulsamad, H.; Naveh, K.; Peters, J. (2019). Model-Based Relative Entropy Policy Search for Stochastic Hybrid Systems, 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM).
•       Bib
  Abi Farraj, F.; Pacchierotti, C.; Arenz, O.; Neumann, G.; Giordano, P. (2019). A Haptic Shared-Control Architecture for Guided Multi-Target Robotic Grasping, IEEE Transactions on Haptics.
•     Bib
  Akrour, R.; Pajarinen, J.; Neumann, G.; Peters, J. (2019). Projections for Approximate Policy Iteration Algorithms, Proceedings of the International Conference on Machine Learning (ICML).
•     Bib
  Becker-Ehmck, P.; Peters, J.; van der Smagt, P. (2019). Switching Linear Dynamics for Variational Bayes Filtering, Proceedings of the International Conference on Machine Learning (ICML).
•     Bib
  Belousov, B.; Abdulsamad, H.; Schultheis, M.; Peters, J. (2019). Belief Space Model Predictive Control for Approximately Optimal System Identification, 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM).
•       Bib
  Belousov, B.; Peters, J. (2019). Entropic Regularization of Markov Decision Processes, Entropy, 21, 7, MDPI.
•     Bib
  Belousov, B.; Sadybakasov, A.; Wibranek, B.; Veiga, F.; Tessmann, O.; Peters, J. (2019). Building a Library of Tactile Skills Based on FingerVision, Proceedings of the 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids).
•     Bib
  Brandherm, F.; Peters, J.; Neumann, G.; Akrour, R. (2019). Learning Replanning Policies with Direct Policy Search, IEEE Robotics and Automation Letters (RA-L).
•     Bib
  Celemin, C.; Maeda, G.; Peters, J.; Ruiz-del-Solar, J.; Kober, J. (2019). Reinforcement Learning of Motor Skills using Policy Search and Human Corrective Advice, International Journal of Robotics Research (IJRR), 38, 14.
•     Bib
  Celik, O.; Abdulsamad, H.; Peters, J. (2019). Chance-Constrained Trajectory Optimization for Nonlinear Systems with Unknown Stochastic Dynamics, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Delfosse, Q.; Stark, S.; Tanneberg, D.; Santucci, V. G.; Peters, J. (2019). Open-Ended Learning of Grasp Strategies using Intrinsically Motivated Self-Supervision, Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•       Bib
  Ewerton, M.; Arenz, O.; Maeda, G.; Koert, D.; Kolev, Z.; Takahashi, M.; Peters, J. (2019). Learning Trajectory Distributions for Assisted Teleoperation and Path Planning, Frontiers in Robotics and AI.
•     Bib
  Ewerton, M.; Maeda, G.; Koert, D.; Kolev, Z.; Takahashi, M.; Peters, J. (2019). Reinforcement Learning of Trajectory Distributions: Applications in Assisted Teleoperation and Motion Planning, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.4294--4300.
•     Bib
  Gebhardt, G.H.W.; Kupcsik, A.; Neumann, G. (2019). The Kernel Kalman Rule, Machine Learning Journal (MLJ), 108, 12, pp.2113–2157, Springer US.
•     Bib
  Gomez Gonzalez, S.; Nemmour, Y.; Schoelkopf, B.; Peters, J. (2019). Reliable Real Time Ball Tracking for Robot Table Tennis, Robotics, 8, 4.
•     Bib
  Klink, P.; Abdulsamad, H.; Belousov, B.; Peters, J. (2019). Self-Paced Contextual Reinforcement Learning, Proceedings of the 3rd Conference on Robot Learning (CoRL).
•   Bib
  Klink, P.; Peters, J. (2019). Measuring Similarities between Markov Decision Processes, 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM).
•     Bib
  Koc, O.; Maeda, G.; Peters, J. (2019). Optimizing the Execution of Dynamic Robot Movements with Learning Control, IEEE Transactions on Robotics, 35, 4, pp.1552-3098.
•     Bib
  Koc, O.; Peters, J. (2019). Learning to serve: an experimental study for a new learning from demonstrations framework, IEEE Robotics and Automation Letters (ICRA/RA-L), with Presentation at the IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Koert, D.; Pajarinen, J.; Schotschneider, A.; Trick, S., Rothkopf, C.; Peters, J. (2019). Learning Intention Aware Online Adaptation of Movement Primitives, IEEE Robotics and Automation Letters (RA-L), with presentation at the IEEE International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Lauri, M.; Pajarinen, J.; Peters, J. (2019). Information gathering in decentralized POMDPs by policy graph improvement, Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
•     Bib
  Liu, Z.; Hitzmann, A.; Ikemoto, S.; Stark, S.; Peters, J.; Hosoda, K. (2019). Local Online Motor Babbling: Learning Motor Abundance of a Musculoskeletal Robot Arm, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•   Bib
  Look, A.; Kandemir, M. (2019). Differential Bayesian Neural Nets, NeurIPS Bayesian Workshop.
•     Bib
  Lutter, M.; Belousov, B.; Listmann, K.; Clever, D.; Peters, J. (2019). HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints, Conference on Robot Learning (CoRL).
•     Bib
  Lutter, M.; Peters, J. (2019). Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems, International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Lutter, M.; Peters, J. (2019). Deep Optimal Control: Using the Euler-Lagrange Equation to learn an Optimal Feedback Control Law, Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM).
•     Bib
  Lutter, M.; Ritter, C.; Peters, J. (2019). Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning, International Conference on Learning Representations (ICLR).
•     Bib
  Muratore, F.; Gienger, M.; Peters, J. (2019). Assessing Transferability in Reinforcement Learning from Randomized Simulations, Reinforcement Learning and Decision Making (RLDM).
•     Bib
  Nass, D.; Belousov, B.; Peters, J. (2019). Entropic Risk Measure in Policy Search, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Ozdenizci, O.; Meyer, T.; Wichmann, F.; Peters, J.; Schoelkopf B.; Cetin, M.; Grosse-Wentrup, M. (2019). Neural Signatures of Motor Skill in the Resting Brain, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC).
•     Bib
  Pajarinen, J.; Thai, H.L.; Akrour, R.; Peters, J.; Neumann, G. (2019). Compatible natural gradient policy search, Machine Learning (MLJ), 108, 8, pp.1443--1466, Springer.
•     Bib
  Parisi, S.; Tangkaratt, V.; Peters, J.; Khan, M. E. (2019). TD-Regularized Actor-Critic Methods, Machine Learning (MLJ), 108, 8, pp.1467-1501.
•   Bib
  Schuermann, T.; Mohler, B.J.; Peters, J.; Beckerle, P. (2019). How Cognitive Models of Human Body Experience Might Push Robotics, Frontiers in Neurorobotics.
•     Bib
  Schultheis, M.; Belousov, B.; Abdulsamad, H.; Peters, J. (2019). Receding Horizon Curiosity, Proceedings of the 3rd Conference on Robot Learning (CoRL).
•     Bib
  Stark, S.; Peters, J.; Rueckert, E. (2019). Experience Reuse with Probabilistic Movement Primitives, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•       Bib
  Tanneberg, D.; Peters, J.; Rueckert, E. (2019). Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks, Neural Networks, 109, pp.67-80.
•     Bib
  Tanneberg, D.; Rueckert, E.; Peters, J. (2019). Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer Architecture, arXiv.
•     Bib
  Tosatto, S.; D'Eramo, C.; Pajarinen, J.; Restelli, M.; Peters, J. (2019). Exploration Driven By an Optimistic Bellman Equation, Proceedings of the International Joint Conference on Neural Networks (IJCNN).
•     Bib
  Trick, S.; Koert, D.; Peters, J.; Rothkopf, C. (2019). Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Urain, J.; Peters, J. (2019). Generalized Multiple Correlation Coefficient as a Similarity Measurement between Trajectories, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Watson, J.; Abdulsamad, H.; Peters, J. (2019). Stochastic Optimal Control as Approximate Input Inference, Conference on Robot Learning (CoRL).
•     Bib
  Wibranek, B.; Belousov, B.; Sadybakasov, A.; Peters, J.; Tessmann, O. (2019). Interactive Structure: Robotic Repositioning of Vertical Elements in Man-Machine Collaborative Assembly through Vision-Based Tactile Sensing, Proceedings of the 37th eCAADe and 23rd SIGraDi Conference.
•     Bib
  Wibranek, B.; Belousov, B.; Sadybakasov, A.; Tessmann, O. (2019). Interactive Assemblies: Man-Machine Collaboration through Building Components for As-Built Digital Models, Computer-Aided Architectural Design Futures (CAAD Futures).

2018

•     Bib
  Akrour, R.; Peters, J.; Neumann, G. (2018). Constraint-Space Projection Direct Policy Search, European Workshops on Reinforcement Learning (EWRL).
•     Bib
  Akrour, R.; Veiga, F.; Peters, J.; Neumann, G. (2018). Regularizing Reinforcement Learning with State Abstraction, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Arenz, O.; Zhong, M.; Neumann, G. (2018). Efficient Gradient-Free Variational Inference using Policy Search, in: Dy, Jennifer and Krause, Andreas (eds.), Proceedings of the International Conference on Machine Learning (ICML), 80, pp.234--243, PMLR.
•     Bib
  Belousov, B.; Peters, J. (2018). Entropic Regularization of Markov Decision Processes, 38th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering.
•     Bib
  Belousov, B.; Peters, J. (2018). Mean Squared Advantage Minimization as a Consequence of Entropic Policy Improvement Regularization, European Workshops on Reinforcement Learning (EWRL).
•     Bib
  Buechler, D.; Calandra, R.; Schoelkopf, B.; Peters, J. (2018). Control of Musculoskeletal Systems using Learned Dynamics Models, IEEE Robotics and Automation Letters, and IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Ewerton, M.; Rother, D.; Weimar, J.; Kollegger, G.; Wiemeyer, J.; Peters, J.; Maeda, G. (2018). Assisting Movement Training and Execution with Visual and Haptic Feedback, Frontiers in Neurorobotics.
•     Bib
  Gebhardt, G.H.W.; Daun, K.; Schnaubelt, M.; Neumann, G. (2018). Learning Robust Policies for Object Manipulation with Robot Swarms, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Gondaliya, K.D.; Peters, J.; Rueckert, E. (2018). Learning to Categorize Bug Reports with LSTM Networks, Proceedings of the International Conference on Advances in System Testing and Validation Lifecycle.
•     Bib
  Hoelscher, J.; Koert, D.; Peters, J.; Pajarinen, J. (2018). Utilizing Human Feedback in POMDP Execution and Specification, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Koc, O.; Maeda, G.; Peters, J. (2018). Online optimal trajectory generation for robot table tennis, Robotics and Autonomous Systems (RAS).
•     Bib
  Koert, D.; Maeda, G.; Neumann, G.; Peters, J. (2018). Learning Coupled Forward-Inverse Models with Combined Prediction Errors, Proceedings of the International Conference on Robotics and Automation (ICRA).
•     Bib
  Koert, D.; Trick, S.; Ewerton, M.; Lutter, M.; Peters, J. (2018). Online Learning of an Open-Ended Skill Library for Collaborative Tasks, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Kroemer, O.; Leischnig, S.; Luettgen, S.; Peters, J. (2018). A Kernel-based Approach to Learning Contact Distributions for Robot Manipulation Tasks, Autonomous Robots (AURO), 42, 3, pp.581-600.
•     Bib
  Lioutikov, R.; Maeda, G.; Veiga, F.F.; Kersting, K.; Peters, J. (2018). Inducing Probabilistic Context-Free Grammars for the Sequencing of Robot Movement Primitives, Proceedings of the International Conference on Robotics and Automation (ICRA).
•     Bib
  Manschitz, S.; Gienger, M.; Kober, J.; Peters, J. (2018). Mixture of Attractors: A novel Movement Primitive Representation for Learning Motor Skills from Demonstrations, IEEE Robotics and Automation Letters (RA-L), 3, 2, pp.926-933.
•       Bib
  Muratore, F.; Treede, F.; Gienger, M.; Peters, J. (2018). Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment, Conference on Robot Learning (CoRL).
•     Bib
  Osa, T.; Pajarinen, J.; Neumann, G.; Bagnell, J.A.; Abbeel, P.; Peters, J. (2018). An Algorithmic Perspective on Imitation Learning, Foundations and Trends in Robotics.
•     Bib
  Osa, T.; Peters, J.; Neumann, G. (2018). Hierarchical Reinforcement Learning of Multiple Grasping Strategies with Human Instructions, Advanced Robotics, 32, 18, pp.955-968.
•     Bib
  Paraschos, A.; Daniel, C.; Peters, J.; Neumann, G. (2018). Using Probabilistic Movement Primitives in Robotics, Autonomous Robots (AURO), 42, 3, pp.529-551.
•     Bib
  Paraschos, A.; Rueckert, E.; Peters, J.; Neumann, G. (2018). Probabilistic Movement Primitives under Unknown System Dynamics, Advanced Robotics (ARJ), 32, 6, pp.297-310.
•   Bib
  Parmas, P.; Doya, K.; Rasmussen, C.; Peters, J. (2018). PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos, Proceedings of the International Conference on Machine Learning (ICML).
•     Bib
  Pinsler, R.; Akrour, R.; Osa, T.; Peters, J.; Neumann, G. (2018). Sample and Feedback Efficient Hierarchical Reinforcement Learning from Human Preferences, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Pinsler, R.; Maag, M.; Arenz, O.; Neumann, G. (2018). Inverse Reinforcement Learning of Bird Flocking Behavior, Swarms: From Biology to Robotics and Back (ICRA Workshop).
•   Bib
  Sosic, A.; Rueckert, E.; Peters, J.; Zoubir, A.M.; Koeppl, H (2018). Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling, Journal of Machine Learning Research (JMLR), 19, 69, pp.1--45.
•     Bib
  Veiga, F.; Peters, J.; Hermans, T. (2018). Grip Stabilization of Novel Objects using Slip Prediction, IEEE Transactions on Haptics, 11, 4, pp.531--542.
•     Bib
  Vinogradska, J.; Bischoff, B.; Peters, J. (2018). Approximate Value Iteration based on Numerical Quadrature, Proceedings of the International Conference on Robotics and Automation, and IEEE Robotics and Automation Letters (RA-L), 3, pp.1330-1337.
•     Bib
  Yi, Z.; Zhang, Y.; Peters, J. (2018). Biomimetic Tactile Sensors and Signal Processing with Spike Trains: A Review, Sensors & Actuators: A. Physical, 269, pp.41-52.

2017

•     Bib
  Abdulsamad, H.; Arenz, O.; Peters, J.; Neumann, G. (2017). State-Regularized Policy Search for Linearized Dynamical Systems, Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS).
•     Bib
  Akrour, R.; Sorokin, D.; Peters, J.; Neumann, G. (2017). Local Bayesian Optimization of Motor Skills, Proceedings of the International Conference on Machine Learning (ICML).
•     Bib
  Belousov, B.; Neumann, G.; Rothkopf, C.A.; Peters, J. (2017). Catching Heuristics Are Optimal Control Policies, Proceedings of the Karniel Thirteenth Computational Motor Control Workshop.
•     Bib
  Belousov, B.; Peters, J. (2017). f-Divergence Constrained Policy Improvement, arXiv.
•   Bib
  Busch, B.; Maeda, G.; Mollard, Y.; Demangeat, M.; Lopes, M. (2017). Postural Optimization for an Ergonomic Human-Robot Interaction, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Dermy, O.; Paraschos, A.; Ewerton, M.; Charpillet, F.; Peters, J.; Ivaldi, S (2017). Prediction of intention during interaction with iCub with Probabilistic Movement Primitives, Frontiers in Robotics and AI, 4, pp.45.
•     Bib
  End, F.; Akrour, R.; Peters, J.; Neumann, G. (2017). Layered Direct Policy Search for Learning Hierarchical Skills, Proceedings of the International Conference on Robotics and Automation (ICRA).
•     Bib
  Ewerton, M.; Kollegger, G.; Maeda, G.; Wiemeyer, J.; Peters, J. (2017). Iterative Feedback-basierte Korrekturstrategien beim Bewegungslernen von Mensch-Roboter-Dyaden, DVS Sportmotorik 2017.
•     Bib
  Ewerton, M.; Maeda, G.; Rother, D.; Weimar, J.; Lotter, L.; Kollegger, G.; Wiemeyer, J.; Peters, J. (2017). Assisting the practice of motor skills by humans with a probability distribution over trajectories, Workshop Human-in-the-loop robotic manipulation: on the influence of the human role at IROS 2017, Vancouver, Canada.
•     Bib
  Farraj, F. B.; Osa, T.; Pedemonte, N.; Peters, J.; Neumann, G.; Giordano, P.R. (2017). A Learning-based Shared Control Architecture for Interactive Task Execution, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
•   Bib
  Fiebig, K.H.; Jayaram, V.; Hesse, T.; Blank, A.; Peters, J.; Grosse-Wentrup, M. (2017). Bayesian Regression for Artifact Correction in Electroencephalography, Proceedings of the 7th Graz Brain-Computer Interface Conference.
•     Bib
  Gabriel, A.; Akrour, R.; Peters, J.; Neumann, G. (2017). Empowered Skills, Proceedings of the International Conference on Robotics and Automation (ICRA).
•       Bib
  Gebhardt, G.H.W.; Daun, K.; Schnaubelt, M.; Hendrich, A.; Kauth, D.; Neumann, G. (2017). Learning to Assemble Objects with a Robot Swarm, Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), pp.1547--1549, International Foundation for Autonomous Agents and Multiagent Systems.
•     Bib
  Gebhardt, G.H.W.; Kupcsik, A.G.; Neumann, G. (2017). The Kernel Kalman Rule - Efficient Nonparametric Inference with Recursive Least Squares, Proceedings of the National Conference on Artificial Intelligence (AAAI).
•   Bib
  Grossberger, L.; Hohmann, M.R.; Peters J.; Grosse-Wentrup, M. (2017). Investigating Music Imagery as a Cognitive Paradigm for Low-Cost Brain-Computer Interfaces, Proceedings of the 7th Graz Brain-Computer Interface Conference.
•     Bib
  Ivaldi, S.; Lefort, S.; Peters, J.; Chetouani, M.; Provasi, J.; Zibetti, E. (2017). Towards Engagement Models that Consider Individual Factors in HRI: On the Relation of Extroversion and Negative Attitude Towards Robots to Gaze and Speech During a Human-Robot Assembly Task, International Journal of Social Robotics, 9, pp.63-86.
•   Bib
  Kollegger, G., Wiemeyer, J., Ewerton, M. & Peters, J. (2017). BIMROB - Bidirectional Interaction between human and robot for the learning of movements - Robot trains human - Human trains robot, in: A. Schwirtz, F. Mess, Y. Demetriou & V. Senner (eds.), Inovation & Technologie im Sport - 23. Sportwissenschaftlicher Hochschultag der deutschen Vereinigung für Sportwissenschaft, pp.179, Czwalina-Feldhaus.
•     Bib
  Kollegger, G.; Ewerton, M.; Wiemeyer, J.; Peters, J. (2017). BIMROB – Bidirectional Interaction between human and robot for the learning of movements – Robot trains human – Human trains robot, 23. Sport­wissenschaft­licher Hochschultag der dvs.
•     Bib
  Kollegger, G.; Ewerton, M.; Wiemeyer, J.; Peters, J. (2017). BIMROB -- Bidirectional Interaction Between Human and Robot for the Learning of Movements, in: Lames, M.; Saupe, D.; Wiemeyer, J. (eds.), Proceedings of the 11th International Symposium on Computer Science in Sport (IACSS 2017), pp.151--163, Springer International Publishing.
•     Bib
  Kollegger, G.; Reinhardt, N.; Ewerton, M.; Peters, J.; Wiemeyer, J. (2017). Die Bedeutung der Beobachtungsperspektive beim Bewegungslernen von Mensch-Roboter-Dyaden, DVS Sportmotorik 2017.
•     Bib
  Kroemer, O.; Peters, J. (2017). A Comparison of Autoregressive Hidden Markov Models for Multi-Modal Manipulations with Variable Masses, Proceedings of the International Conference of Robotics and Automation, and IEEE Robotics and Automation Letters (RA-L), 2, 2, pp.1101 - 1108.
•     Bib
  Kupcsik, A.G.; Deisenroth, M.P.; Peters, J.; Ai Poh, L.; Vadakkepat, V.; Neumann, G. (2017). Model-based Contextual Policy Search for Data-Efficient Generalization of Robot Skills, Artificial Intelligence, 247, pp.415-439.
•     Bib
  Lioutikov, R.; Neumann, G.; Maeda, G.; Peters, J. (2017). Learning Movement Primitive Libraries through Probabilistic Segmentation, International Journal of Robotics Research (IJRR), 36, 8, pp.879-894.
•     Bib
  Maeda, G.; Ewerton, M.; Neumann, G.; Lioutikov, R.; Peters, J. (2017). Phase Estimation for Fast Action Recognition and Trajectory Generation in Human-Robot Collaboration, International Journal of Robotics Research (IJRR), 36, 13-14, pp.1579-1594.
•     Bib
  Maeda, G.; Ewerton, M.; Osa, T.; Busch, B.; Peters, J. (2017). Active Incremental Learning of Robot Movement Primitives, Proceedings of the Conference on Robot Learning (CoRL).
•     Bib
  Maeda, G.; Neumann, G.; Ewerton, M.; Lioutikov, R.; Kroemer, O.; Peters, J. (2017). Probabilistic Movement Primitives for Coordination of Multiple Human-Robot Collaborative Tasks, Autonomous Robots (AURO), 41, 3, pp.593-612.
•     Bib
  Osa, T.; Ghalamzan, E. A. M.; Stolkin, R.; Lioutikov, R.; Peters, J.; Neumann, G. (2017). Guiding Trajectory Optimization by Demonstrated Distributions, IEEE Robotics and Automation Letters (RA-L), 2, 2, pp.819-826, IEEE.
•     Bib
  Padois, V.; Ivaldib, S.; Babič, J.; Mistry, M.; Peters, J.; Nori, F. (2017). Whole-body multi-contact motion in humans and humanoids, Robotics and Autonomous Systems, 90, pp.97-117.
•     Bib
  Pajarinen, J.; Kyrki, V.; Koval, M.; Srinivasa, S; Peters, J.; Neumann, G. (2017). Hybrid Control Trajectory Optimization under Uncertainty, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Paraschos, A.; Lioutikov, R.; Peters, J.; Neumann, G. (2017). Probabilistic Prioritization of Movement Primitives, Proceedings of the International Conference on Intelligent Robot Systems, and IEEE Robotics and Automation Letters (RA-L).
•     Bib
  Parisi, S.; Pirotta, M.; Peters, J. (2017). Manifold-based Multi-objective Policy Search with Sample Reuse, Neurocomputing, 263, pp.3-14.
•     Bib
  Parisi, S.; Ramstedt, S.; Peters, J. (2017). Goal-Driven Dimensionality Reduction for Reinforcement Learning, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
•   Bib
  Peters, J.; Lee, D.; Kober, J.; Nguyen-Tuong, D.; Bagnell, J.; Schaal, S. (2017). Chapter 15: Robot Learning, Springer Handbook of Robotics, 2nd Edition, pp.357-394, Springer International Publishing.
•     Bib
  Rueckert, E.; Nakatenus, M.; Tosatto, S.; Peters, J. (2017). Learning Inverse Dynamics Models in O(n) time with LSTM networks, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Stark, S.; Peters, J.; Rueckert, E. (2017). A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Tangkaratt, V.; van Hoof, H.; Parisi, S.; Neumann, G.; Peters, J.; Sugiyama, M. (2017). Policy Search with High-Dimensional Context Variables, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
•     Bib
  Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals, Proceedings of the Conference on Robot Learning (CoRL).
•     Bib
  Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Efficient Online Adaptation with Stochastic Recurrent Neural Networks, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Thiem, S.; Stark, S.; Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Simulation of the underactuated Sake Robotics Gripper in V-REP, Workshop at the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Tosatto, S.; Pirotta, M.; D'Eramo, C; Restelli, M. (2017). Boosted Fitted Q-Iteration, Proceedings of the International Conference of Machine Learning (ICML).
•     Bib
  van Hoof, H.; Neumann, G.; Peters, J. (2017). Non-parametric Policy Search with Limited Information Loss, Journal of Machine Learning Research (JMLR), 18, 73, pp.1-46.
•     Bib
  van Hoof, H.; Tanneberg, D.; Peters, J. (2017). Generalized Exploration in Policy Search, Machine Learning (MLJ), 106, 9-10, pp.1705-1724.
•     Bib
  Vinogradska, J.; Bischoff, B.; Nguyen-Tuong, D.; Peters, J. (2017). Stability of Controllers for Gaussian Process Forward Models, Journal of Machine Learning Research (JMLR), 18, 100, pp.1-37.
•     Bib
  Wang, Z.; Boularias, A.; Muelling, K.; Schoelkopf, B.; Peters, J. (2017). Anticipatory Action Selection for Human-Robot Table Tennis, Artificial Intelligence, 247, pp.399-414.
•     Bib
  Wiemeyer, J.; Peters, J.; Kollegger, G.; Ewerton, M. (2017). BIMROB – Bidirektionale Interaktion von Mensch und Roboter beim Bewegungslernen, DVS Sportmotorik 2017.
•     Bib
  Wilbers, D.; Lioutikov, R.; Peters, J. (2017). Context-Driven Movement Primitive Adaptation, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Yi, Z.; Zhang, Y.; Peters, J. (2017). Bioinspired Tactile Sensor for Surface Roughness Discrimination, Sensors and Actuators A: Physical, 255, pp.46-53.

2016

•     Bib
  Abdolmaleki, A.; Lau, N.; Reis, L.; Peters, J.; Neumann, G. (2016). Contextual Policy Search for Linear and Nonlinear Generalization of a Humanoid Walking Controller, Journal of Intelligent & Robotic Systems.
•     Bib
  Akrour, R.; Abdolmaleki, A.; Abdulsamad, H.; Neumann, G. (2016). Model-Free Trajectory Optimization for Reinforcement Learning, Proceedings of the International Conference on Machine Learning (ICML).
•     Bib
  Arenz, O.; Abdulsamad, H.; Neumann, G. (2016). Optimal Control and Inverse Optimal Control by Distribution Matching, Proceedings of the International Conference on Intelligent Robots and Systems (IROS), IEEE.
•     Bib
  Arenz, O.; Abdulsamad, H.; Neumann, G. (2016). (Inverse) Optimal Control for Matching Higher-Order Moments, DGR Days (Leipzig).
•       Bib
  Arenz, O.; Neumann, G. (2016). Iterative Cost Learning from Different Types of Human Feedback, IROS 2016 Workshop on Human-Robot Collaboration.
•     Bib
  Azad, M.; Ortenzi, V.; Lin, H., C.; Rueckert, E.; Mistry, M. (2016). Model Estimation and Control of Complaint Contact Normal Force, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Belousov, B.; Neumann, G.; Rothkopf, C.; Peters, J. (2016). Catching Heuristics Are Optimal Control Policies, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
•   Bib
  Buechler, D.; Ott, H.; Peters, J. (2016). A Lightweight Robotic Arm with Pneumatic Muscles for Robot Learning, Proceedings of the International Conference on Robotics and Automation (ICRA).
•     Bib
  Calandra, R.; Peters, J.; Rasmussen, C.E.; Deisenroth, M.P. (2016). Manifold Gaussian Processes for Regression, Proceedings of the International Joint Conference on Neural Networks (IJCNN).
•     Bib
  Daniel, C.; Neumann, G.; Kroemer, O.; Peters, J. (2016). Hierarchical Relative Entropy Policy Search, Journal of Machine Learning Research (JMLR), 17, pp.1-50.
•     Bib
  Daniel, C.; van Hoof, H.; Peters, J.; Neumann, G. (2016). Probabilistic Inference for Determining Options in Reinforcement Learning, Machine Learning (MLJ), 104, 2-3, pp.337-357.
•     Bib
  Ewerton, M.; Maeda, G.; Neumann, G.; Kisner, V.; Kollegger, G.; Wiemeyer, J.; Peters, J. (2016). Movement Primitives with Multiple Phase Parameters, Proceedings of the International Conference on Robotics and Automation (ICRA), pp.201--206.
•     Bib
  Ewerton, M.; Maeda, G.J.; Kollegger, G.; Wiemeyer, J.; Peters, J. (2016). Incremental Imitation Learning of Context-Dependent Motor Skills, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), pp.351--358.
•     Bib
  Fiebig, K.-H.; Jayaram, V.; Peters, J.; Grosse-Wentrup, M. (2016). Multi-Task Logistic Regression in Brain-Computer Interfaces, IEEE SMC 2016 — 6th Workshop on Brain-Machine Interface Systems.
•   Bib
  Gomez-Gonzalez, S.; Neumann, G.; Schoelkopf, B.; Peters, J. (2016). Using Probabilistic Movement Primitives for Striking Movements, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•   Bib
  Huang, Y.; Buechler, D.; Koc, O.; Schoelkopf, B.; Peters, J. (2016). Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•   Bib
  Koc, O.; Peters, J.; Maeda, G. (2016). A New Trajectory Generation Framework in Robotic Table Tennis, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Koert, D.; Maeda, G.J.; Lioutikov, R.; Neumann, G.; Peters, J. (2016). Demonstration Based Trajectory Optimization for Generalizable Robot Motions, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Kohlschuetter, J.; Peters, J.; Rueckert, E. (2016). Learning Probabilistic Features from EMG Data for Predicting Knee Abnormalities, Proceedings of the XIV Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON).
•     Bib
  Kollegger, G.; Ewerton, M.; Peters, J.; Wiemeyer, J. (2016). Bidirektionale Interaktion zwischen Mensch und Roboter beim Bewegungslernen (BIMROB), 11. Symposium der DVS Sportinformatik.
•   Bib
  Maeda, G.; Maloo, A.; Ewerton, M.; Lioutikov, R.; Peters, J. (2016). Proactive Human-Robot Collaboration with Interaction Primitives, International Workshop on Human-Friendly Robotics (HFR), Genoa, Italy.
•     Bib
  Maeda, G.; Ewerton, M.; Koert, D; Peters, J. (2016). Acquiring and Generalizing the Embodiment Mapping from Human Observations to Robot Skills, IEEE Robotics and Automation Letters (RA-L), 1, 2, pp.784--791.
•     Bib
  Maeda, G.; Maloo, A.; Ewerton, M.; Lioutikov, R.; Peters, J. (2016). Anticipative Interaction Primitives for Human-Robot Collaboration, AAAI Fall Symposium Series. Shared Autonomy in Research and Practice, Arlington, VA, USA.
•     Bib
  Manschitz, S.; Gienger, M.; Kober, J.; Peters, J. (2016). Probabilistic Decomposition of Sequential Force Interaction Tasks into Movement Primitives, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Modugno, V.; Neumann, G.; Rueckert, E.; Oriolo, G.; Peters, J.; Ivaldi, S. (2016). Learning soft task priorities for control of redundant robots, Proceedings of the International Conference on Robotics and Automation (ICRA).
•     Bib
  Osa, T.; Peters, J.; Neumann, G. (2016). Experiments with Hierarchical Reinforcement Learning of Multiple Grasping Policies, Proceedings of the International Symposium on Experimental Robotics (ISER).
•     Bib
  Parisi, S; Blank, A; Viernickel T; Peters, J (2016). Local-utopia Policy Selection for Multi-objective Reinforcement Learning, Proceedings of the IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL).
•   Bib
  Peters, J.; Bagnell, J.A. (2016). Policy gradient methods, Encyclopedia of Machine Learning, 2nd Edition, Invited Article.
•   Bib
  Peters, J.; Tedrake, R.; Roy, N.; Morimoto, J. (2016). Robot Learning, Encyclopedia of Machine Learning, 2nd Edition, Invited Article.
•       Bib
  Rueckert, E.; Camernik, J.; Peters, J.; Babic, J. (2016). Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control, Nature PG: Scientific Reports, 6, 28455.
•       Bib
  Rueckert, E.; Kappel, D.; Tanneberg, D.; Pecevski, D; Peters, J. (2016). Recurrent Spiking Networks Solve Planning Tasks, Nature PG: Scientific Reports, 6, 21142, Nature Publishing Group.
•     Bib
  Sharma, D.; Tanneberg, D.; Grosse-Wentrup, M.; Peters, J.; Rueckert, E. (2016). Adaptive Training Strategies for BCIs, Cybathlon Symposium.
•     Bib
  Tanneberg, D.; Paraschos, A.; Peters, J.; Rueckert, E. (2016). Deep Spiking Networks for Model-based Planning in Humanoids, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  van Hoof, H.; Chen, N.; Karl, M.; van der Smagt, P.; Peters, J. (2016). Stable Reinforcement Learning with Autoencoders for Tactile and Visual Data, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Veiga, F.F.; Peters, J. (2016). Can Modular Finger Control for In-Hand Object Stabilization be accomplished by Independent Tactile Feedback Control Laws?, arXiv.
•     Bib
  Vinogradska, J.; Bischoff, B.; Nguyen-Tuong, D.; Romer, A.; Schmidt, H.; Peters, J. (2016). Stability of Controllers for Gaussian Process Forward Models, Proceedings of the International Conference on Machine Learning (ICML).
•     Bib
  Weber, P.; Rueckert, E.; Calandra, R.; Peters, J.; Beckerle, P. (2016). A Low-cost Sensor Glove with Vibrotactile Feedback and Multiple Finger Joint and Hand Motion Sensing for Human-Robot Interaction, Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).
•     Bib
  Yi, Z.; Calandra, R.; Veiga, F.; van Hoof, H.; Hermans, T.; Zhang, Y.; Peters, J. (2016). Active Tactile Object Exploration with Gaussian Processes, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
•   Bib
  Yi, Z.; Zhang, Y.; Peters, J. (2016). Surface Roughness Discrimination Using Bioinspired Tactile Sensors, Proceedings of the 16th International Conference on Biomedical Engineering.

2015

•     Bib
  Abdolmaleki, A.; Lioutikov, R.; Peters, J; Lau, N.; Reis, L.; Neumann, G. (2015). Model-Based Relative Entropy Stochastic Search, Advances in Neural Information Processing Systems (NIPS / NeurIPS), MIT Press.
•     Bib
  Calandra, R.; Ivaldi, S.; Deisenroth, M.; Peters, J. (2015). Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Calandra, R.; Ivaldi, S.; Deisenroth, M.;Rueckert, E.; Peters, J. (2015). Learning Inverse Dynamics Models with Contacts, Proceedings of the International Conference on Robotics and Automation (ICRA).
•       Bib
  Calandra, R.; Seyfarth, A.; Peters, J.; Deisenroth, M. (2015). Bayesian Optimization for Learning Gaits under Uncertainty, Annals of Mathematics and Artificial Intelligence (AMAI).
•     Bib
  Daniel, C.; Kroemer, O.; Viering, M.; Metz, J.; Peters, J. (2015). Active Reward Learning with a Novel Acquisition Function, Autonomous Robots (AURO), 39, pp.389-405.
•   Bib
  Dann, C.; Neumann, G.; Peters, J. (2015). Policy Evaluation with Temporal Differences: A Survey and Comparison, Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling (ICAPS), pp.359-360.
•     Bib
  Ewerton, M.; Maeda, G.J.; Peters, J.; Neumann, G. (2015). Learning Motor Skills from Partially Observed Movements Executed at Different Speeds, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), pp.456--463.
•     Bib
  Ewerton, M.; Neumann, G.; Lioutikov, R.; Ben Amor, H.; Peters, J.; Maeda, G. (2015). Learning Multiple Collaborative Tasks with a Mixture of Interaction Primitives, Proceedings of the International Conference on Robotics and Automation (ICRA), pp.1535--1542.
•     Bib
  Ewerton, M.; Neumann, G.; Lioutikov, R.; Ben Amor, H.; Peters, J.; Maeda, G. (2015). Modeling Spatio-Temporal Variability in Human-Robot Interaction with Probabilistic Movement Primitives, Workshop on Machine Learning for Social Robotics, ICRA.
•     Bib
  Fritsche, L.; Unverzagt, F.; Peters, J.; Calandra, R. (2015). First-Person Tele-Operation of a Humanoid Robot, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Hoelscher, J.; Peters, J.; Hermans, T. (2015). Evaluation of Interactive Object Recognition with Tactile Sensing, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Huang, Y.; Schoelkopf, B.; Peters, J. (2015). Learning Optimal Striking Points for A Ping-Pong Playing Robot, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
•   Bib
  Koc, O.; Maeda, G.; Neumann, G.; Peters, J. (2015). Optimizing Robot Striking Movement Primitives with Iterative Learning Control, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Kroemer, O.; Daniel, C.; Neumann, G; van Hoof, H.; Peters, J. (2015). Towards Learning Hierarchical Skills for Multi-Phase Manipulation Tasks, Proceedings of the International Conference on Robotics and Automation (ICRA).
•     Bib
  Leischnig, S.; Luettgen, S.; Kroemer, O.; Peters, J. (2015). A Comparison of Contact Distribution Representations for Learning to Predict Object Interactions, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Lioutikov, R.; Neumann, G.; Maeda, G.J.; Peters, J. (2015). Probabilistic Segmentation Applied to an Assembly Task, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Lopes, M.; Peters, J.; Piater, J.; Toussaint, M.; Baisero, A.; Busch, B.; Erkent, O.; Kroemer, O.; Lioutikov, R.; Maeda, G.; Mollard, Y.; Munzer, T.; Shukla, D. (2015). Semi-Autonomous 3rd-Hand Robot, Workshop on Cognitive Robotics in Future Manufacturing Scenarios, European Robotics Forum, Vienna, Austria.
•     Bib
  Maeda, G.; Neumann, G.; Ewerton, M.; Lioutikov, R.; Peters, J. (2015). A Probabilistic Framework for Semi-Autonomous Robots Based on Interaction Primitives with Phase Estimation, Proceedings of the International Symposium of Robotics Research (ISRR).
•     Bib
  Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2015). Probabilistic Progress Prediction and Sequencing of Concurrent Movement Primitives, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2015). Learning Movement Primitive Attractor Goals and Sequential Skills from Kinesthetic Demonstrations, Robotics and Autonomous Systems, 74, pp.97-107.
•     Bib
  Mariti, C.; Muscolo, G.G.; Peters, J.; Puig, D.; Recchiuto, C.T.; Sighieri, C.; Solanas, A.; von Stryk, O. (2015). Developing biorobotics for veterinary research into cat movements, Journal of Veterinary Behavior: Clinical Applications and Research, 10, pp.248-254.
•     Bib
  Paraschos, A.; Rueckert, E.; Peters, J; Neumann, G. (2015). Model-Free Probabilistic Movement Primitives for Physical Interaction, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Parisi, S.; Abdulsamad, H.; Paraschos, A.; Daniel, C.; Peters, J. (2015). Reinforcement Learning vs Human Programming in Tetherball Robot Games, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
•       Bib
  Rueckert, E.; Lioutikov, R.; Calandra, R.; Schmidt, M.; Beckerle, P.; Peters, J. (2015). Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations, ICRA 2015 Workshop on Tactile and force sensing for autonomous compliant intelligent robots.
•     Bib
  Rueckert, E.; Mundo, J.; Paraschos, A.; Peters, J.; Neumann, G. (2015). Extracting Low-Dimensional Control Variables for Movement Primitives, Proceedings of the International Conference on Robotics and Automation (ICRA).
•     Bib
  Traversaro, S.; Del Prete, A.; Ivaldi, S.; Nori, F. (2015). Avoiding to rely on Inertial Parameters in Estimating Joint Torques with proximal F/T sensing, Proceedings of the International Conference on Robotics and Automation (ICRA).
•       Bib
  van Hoof, H.; Hermans, T.; Neumann, G.; Peters, J. (2015). Learning Robot In-Hand Manipulation with Tactile Features, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  van Hoof, H.; Peters, J.; Neumann, G. (2015). Learning of Non-Parametric Control Policies with High-Dimensional State Features, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).
•     Bib
  Veiga, F.F.; van Hoof, H.; Peters, J.; Hermans, T. (2015). Stabilizing Novel Objects by Learning to Predict Tactile Slip, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Wahrburg, A.; Zeiss, S.; Matthias, B.; Peters, J.; Ding, H. (2015). Combined Pose-Wrench and State Machine Representation for Modeling Robotic Assembly Skills, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).

2014

•     Bib
  Ben Amor, H.; Neumann, G.; Kamthe, S.; Kroemer, O.; Peters, J. (2014). Interaction Primitives for Human-Robot Cooperation Tasks , Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
•   Bib
  Ben Amor, H.; Saxena, A.; Hudson, N.; Peters, J. (2014). Special issue on autonomous grasping and manipulation, Autonomous Robots (AURO).
•     Bib
  Bischoff, B.; Nguyen-Tuong, D.; van Hoof, H. McHutchon, A.; Rasmussen, C.E.; Knoll, A.; Peters, J.; Deisenroth, M.P. (2014). Policy Search For Learning Robot Control Using Sparse Data, Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Bocsi, B.; Csato, L.; Peters, J. (2014). Indirect Robot Model Learning for Tracking Control, Advanced Robotics.
•     Bib
  Brandl, S.; Kroemer, O.; Peters, J. (2014). Generalizing Pouring Actions Between Objects using Warped Parameters, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Calandra, R.; Gopalan, N.; Seyfarth, A.; Peters, J.; Deisenroth, M.P. (2014). Bayesian Gait Optimization for Bipedal Locomotion, Proceedings of the 2014 Learning and Intelligent Optimization Conference (LION8).
•     Bib
  Calandra, R.; Seyfarth, A.; Peters, J.; Deisenroth, M.P. (2014). An Experimental Comparison of Bayesian Optimization for Bipedal Locomotion, Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Chebotar, Y.; Kroemer, O.; Peters, J. (2014). Learning Robot Tactile Sensing for Object Manipulation, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Colome, A.; Neumann, G.; Peters, J.; Torras, C. (2014). Dimensionality Reduction for Probabilistic Movement Primitives, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Daniel, C.; Viering, M.; Metz, J.; Kroemer, O.; Peters, J. (2014). Active Reward Learning, Proceedings of Robotics: Science & Systems (R:SS).
•     Bib
  Dann, C.; Neumann, G.; Peters, J. (2014). Policy Evaluation with Temporal Differences: A Survey and Comparison, Journal of Machine Learning Research (JMLR), 15, March, pp.809-883.
•     Bib
  Deisenroth, M.P.; Englert, P.; Peters, J.; Fox, D. (2014). Multi-Task Policy Search for Robotics, Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
•       Bib
  Deisenroth, M.P.; Fox, D.; Rasmussen, C.E. (2014). Gaussian Processes for Data-Efficient Learning in Robotics and Control, IEEE Transactions on Pattern Analysis and Machine Intelligence.
•     Bib
  Droniou, A.; Ivaldi, S.; Sigaud, O. (2014). Deep unsupervised network for multimodal perception, representation and classification, Robotics and Autonomous Systems.
•     Bib
  Gomez, V.; Kappen, B; Peters, J.; Neumann, G (2014). Policy Search for Path Integral Control, Proceedings of the European Conference on Machine Learning (ECML).
•     Bib
  Haji Ghassemi, N.; Deisenroth, M.P. (2014). Approximate Inference for Long-Term Forecasting with Periodic Gaussian Processes, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).
•     Bib
  Hermans, T.; Veiga, F.; Hoelscher, J.; van Hoof, H.; Peters, J. (2014). Demonstration: Learning for Tactile Manipulation, Advances in Neural Information Processing Systems (NIPS/NeurIPS), Demonstration Track., MIT Press.
•     Bib
  Ivaldi, S.; Peters, J.; Padois, V.; Nori, F. (2014). Tools for simulating humanoid robot dynamics: a survey based on user feedback, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Kamthe, S.; Peters, J.; Deisenroth, M. (2014). Multi-modal filtering for non-linear estimation, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).
•   Bib
  Kober, J.; Peters, J. (2014). Learning Motor Skills - From Algorithms to Robot Experiments, Springer Tracts in Advanced Robotics 97 (STAR Series), Springer .
•     Bib
  Kroemer, O.; Peters, J. (2014). Predicting Object Interactions from Contact Distributions, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Kroemer, O.; van Hoof, H.; Neumann, G.; Peters, J. (2014). Learning to Predict Phases of Manipulation Tasks as Hidden States, Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Lioutikov, R.; Kroemer, O.; Peters, J.; Maeda, G. (2014). Learning Manipulation by Sequencing Motor Primitives with a Two-Armed Robot, Proceedings of the 13th International Conference on Intelligent Autonomous Systems (IAS).
•     Bib
  Lioutikov, R.; Paraschos, A.; Peters, J.; Neumann, G. (2014). Generalizing Movements with Information Theoretic Stochastic Optimal Control, Journal of Aerospace Information Systems, 11, 9, pp.579-595.
•     Bib
  Lioutikov, R.; Paraschos, A.; Peters, J.; Neumann, G. (2014). Sample-Based Information-Theoretic Stochastic Optimal Control, Proceedings of the International Conference on Robotics and Automation (ICRA).
•     Bib
  Luck, K.S.; Neumann, G.; Berger, E.; Peters, J.; Ben Amor, H. (2014). Latent Space Policy Search for Robotics, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Maeda, G.J.; Ewerton, M.; Lioutikov, R.; Amor, H.B.; Peters, J.; Neumann, G. (2014). Learning Interaction for Collaborative Tasks with Probabilistic Movement Primitives, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), pp.527--534.
•   Bib
  Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2014). Learning to Unscrew a Light Bulb from Demonstrations, Proceedings of ISR/ROBOTIK 2014.
•     Bib
  Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2014). Learning to Sequence Movement Primitives from Demonstrations, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
•       Bib
  Meyer, T.; Peters, J.; Zander, T.O.; Schoelkopf, B.; Grosse-Wentrup, M. (2014). Predicting Motor Learning Performance from Electroencephalographic Data, Journal of Neuroengineering and Rehabilitation, 11, 1.
•     Bib
  Muelling, K.; Boularias, A.; Schoelkopf, B.; Peters, J. (2014). Learning Strategies in Table Tennis using Inverse Reinforcement Learning, Biological Cybernetics, 108, 5, pp.603-619.
•     Bib
  Neumann, G.; Daniel, C.; Paraschos, A.; Kupcsik, A.; Peters, J. (2014). Learning Modular Policies for Robotics, Frontiers in Computational Neuroscience.
•     Bib
  Nori, F.; Peters, J.; Padois, V.; Babic, J.; Mistry, M.; Ivaldi, S. (2014). Whole-body motion in humans and humanoids, Proceedings of the Workshop on New Research Frontiers for Intelligent Autonomous Systems (NRF-IAS), pp.81-92.
•     Bib
  Rueckert, E.; Mindt, M.; Peters, J.; Neumann, G. (2014). Robust Policy Updates for Stochastic Optimal Control, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•     Bib
  Saut, J.-P.; Ivaldi, S.; Sahbani, A.; Bidaud, P. (2014). Grasping objects localized from uncertain point cloud data, Robotics and Autonomous Systems.
•       Bib
  van Hoof, H.; Kroemer, O; Peters, J. (2014). Probabilistic Segmentation and Targeted Exploration of Objects in Cluttered Environments, IEEE Transactions on Robotics (TRo), 30, 5, pp.1198-1209.
•     Bib
  Wierstra, D.; Schaul, T.; Glasmachers, T.; Sun, Y.; Peters, J.; Schmidhuber, J. (2014). Natural Evolution Strategies, Journal of Machine Learning Research (JMLR), 15, March, pp.949-980.

2013

•     Bib
  Ben Amor, H.; Vogt, D.; Ewerton, M.; Berger, E.; Jung, B.; Peters, J. (2013). Learning Responsive Robot Behavior by Imitation, Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Berger, E.; Vogt, D.; Haji-Ghassemi, N.; Jung, B.; Ben Amor, H. (2013). Inferring Guidance Information in Cooperative Human-Robot Tasks, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•       Bib
  Bocsi, B.; Csato, L.; Peters, J. (2013). Alignment-based Transfer Learning for Robot Models, Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN) .
•       Bib
  Daniel, C.; Neumann, G.; Kroemer, O.; Peters, J. (2013). Learning Sequential Motor Tasks, Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA).
•   Bib
  Daniel, C.; Neumann, G.; Peters, J. (2013). Autonomous Reinforcement Learning with Hierarchical REPS, Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN) .
•       Bib
  Deisenroth, M. P.; Neumann, G.; Peters, J. (2013). A Survey on Policy Search for Robotics, Foundations and Trends in Robotics, 21, pp.388-403.
•       Bib
  Englert, P.; Paraschos, A.; Peters, J.; Deisenroth, M. P. (2013). Model-based Imitation Learning by Probabilistic Trajectory Matching, Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA).
•       Bib
  Englert, P.; Paraschos, A.; Peters, J.;Deisenroth, M.P. (2013). Probabilistic Model-based Imitation Learning, Adaptive Behavior Journal, 21, pp.388-403.
•       Bib
  Gopalan, N.; Deisenroth, M. P.; Peters, J. (2013). Feedback Error Learning for Rhythmic Motor Primitives, Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA).
•       Bib
  Kober, J.; Bagnell, D.; Peters, J. (2013). Reinforcement Learning in Robotics: A Survey, International Journal of Robotics Research (IJRR), 32, 11, pp.1238-1274.
•       Bib
  Kupcsik, A.G.; Deisenroth, M.P.; Peters, J.; Neumann, G. (2013). Data-Efficient Generalization of Robot Skills with Contextual Policy Search, Proceedings of the National Conference on Artificial Intelligence (AAAI) .
•       Bib
  Muelling, K.; Kober, J.; Kroemer, O.; Peters, J. (2013). Learning to Select and Generalize Striking Movements in Robot Table Tennis, International Journal of Robotics Research (IJRR), 32, 3, pp.263-279.
•   Bib
  Neumann, G.; Kupcsik, A.G.; Deisenroth, M.P.; Peters, J. (2013). Information-Theoretic Motor Skill Learning, Proceedings of the AAAI 2013 Workshop on Intelligent Robotic Systems.
•     Bib
  Paraschos, A.; Daniel, C.; Peters, J.; Neumann, G (2013). Probabilistic Movement Primitives, Advances in Neural Information Processing Systems (NIPS / NeurIPS), MIT Press.
•     Bib
  Paraschos, A.; Neumann, G; Peters, J. (2013). A Probabilistic Approach to Robot Trajectory Generation, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•       Bib
  Peters, J.; Kober, J.; Muelling, K.; Kroemer, O.; Neumann, G. (2013). Towards Robot Skill Learning: From Simple Skills to Table Tennis, Proceedings of the European Conference on Machine Learning (ECML), Nectar Track.
•   Bib
  Peters, J.; Kober, J.; Muelling, K.; Nguyen-Tuong, D.; Kroemer, O. (2013). Learning Skills with Motor Primitives, Proceedings of the 16th Yale Learning Workshop.
•     Bib
  van Hoof, H.; Kroemer, O; Peters, J. (2013). Probabilistic Interactive Segmentation for Anthropomorphic Robots in Cluttered Environments , Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•       Bib
  Wang, Z.; Muelling, K.; Deisenroth, M. P.; Ben Amor, H.; Vogt, D.; Schoelkopf, B.; Peters, J. (2013). Probabilistic Movement Modeling for Intention Inference in Human-Robot Interaction, International Journal of Robotics Research (IJRR), 32, 7, pp.841-858.

2012

•       Bib
  Ben Amor, H.; Kroemer, O.; Hillenbrand, U.; Neumann, G.; Peters, J. (2012). Generalization of Human Grasping for Multi-Fingered Robot Hands, Proceedings of the International Conference on Robot Systems (IROS).
•       Bib
  Bocsi, B.; Hennig, P.; Csato, L.; Peters, J. (2012). Learning Tracking Control with Forward Models, Proceedings of the International Conference on Robotics and Automation (ICRA).
•       Bib
  Boularias, A.; Kroemer, O.; Peters, J. (2012). Structured Apprenticeship Learning, Proceedings of the European Conference on Machine Learning (ECML).
•       Bib
  Boularias, A.; Kroemer, O.; Peters, J. (2012). Algorithms for Learning Markov Field Policies, Advances in Neural Information Processing Systems 26 (NIPS/NeurIPS), Cambridge, MA: MIT Press., MIT Press.
•     Bib
  Calandra, R.; Raiko, T.; Deisenroth, M.P.; Montesino Pouzols, F. (2012). Learning Deep Belief Networks from Non-Stationary Streams, International Conference on Artificial Neural Networks (ICANN).
•       Bib
  Daniel, C.; Neumann, G.; Peters, J. (2012). Hierarchical Relative Entropy Policy Search, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS 2012).
•       Bib
  Daniel, C.; Neumann, G.; Peters, J. (2012). Learning Concurrent Motor Skills in Versatile Solution Spaces, Proceedings of the International Conference on Robot Systems (IROS).
•       Bib
  Deisenroth M. P.; Szepesvari C.; Peters J. (2012)., in: Deisenroth M. P.; Szepesvari C., Peters J. (eds.), Proceedings of the 10th European Workshop on Reinforcement Learning, 24.
•       Bib
  Deisenroth, M.P.; Calandra, R.; Seyfarth, A.; Peters, J. (2012). Toward Fast Policy Search for Learning Legged Locomotion, Proceedings of the International Conference on Robot Systems (IROS).
•     Bib
  Deisenroth, M.P.; Mohamed, S. (2012). Expectation Propagation in Gaussian Process Dynamical Systems, Advances in Neural Information Processing Systems 26 (NIPS/NeurIPS), Cambridge, MA: MIT Press., The MIT Press.
•     Bib
  Deisenroth, M.P.; Peters, J. (2012). Solving Nonlinear Continuous State-Action-Observation POMDPs for Mechanical Systems with Gaussian Noise, Proceedings of the European Workshop on Reinforcement Learning (EWRL).
•     Bib
  Deisenroth, M.P.; Turner, R.; Huber, M.; Hanebeck, U.D.; Rasmussen, C.E (2012). Robust Filtering and Smoothing with Gaussian Processes, IEEE Transactions on Automatic Control.
•       Bib
  Kober, J.; Wilhelm, A.; Oztop, E.; Peters, J. (2012). Reinforcement Learning to Adjust Parametrized Motor Primitives to New Situations, Autonomous Robots (AURO), 33, 4, pp.361-379, Springer US.
•       Bib
  Kober, J; Muelling, K.; Peters, J. (2012). Learning Throwing and Catching Skills, Proceedings of the International Conference on Robot Systems (IROS), Video Track.
•       Bib
  Kroemer, O.; Ben Amor, H.; Ewerton, M.; Peters, J. (2012). Point Cloud Completion Using Extrusions, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
•       Bib
  Kroemer, O.; Ugur, E.; Oztop, E. ; Peters, J. (2012). A Kernel-based Approach to Direct Action Perception, Proceedings of the International Conference on Robotics and Automation (ICRA).
•       Bib
  Lampert, C.H.; Peters, J. (2012). Real-Time Detection of Colored Objects In Multiple Camera Streams With Off-the-Shelf Hardware Components, Journal of Real-Time Image Processing, 7, 1, pp.31-41.
•   Bib
  Meyer, T.; Peters, J.; Broetz, D.; Zander, T.; Schoelkopf, B.; Soekadar, S.; Grosse-Wentrup, M. (2012). Investigating the Neural Basis for Stroke Rehabilitation by Brain-Computer Interfaces, International Conference on Neurorehabilitation.
•       Bib
  Meyer, T.; Peters, J.;Broetz, D.; Zander, T.; Schoelkopf, B.; Soekadar, S.; Grosse-Wentrup, M. (2012). A Brain-Robot Interface for Studying Motor Learning after Stroke, Proceedings of the International Conference on Robot Systems (IROS).
•       Bib
  Muelling, K.; Kober, J.; Kroemer, O.; Peters, J. (2012). Learning to Select and Generalize Striking Movements in Robot Table Tennis, Proceedings of the AAAI 2012 Fall Symposium on Robots that Learn Interactively from Human Teachers.
•       Bib
  Nguyen-Tuong, D.; Peters, J. (2012). Online Kernel-based Learning for Task-Space Tracking Robot Control, IEEE Transactions on Neural Networks and Learning Systems, 23, 9, pp.1417-1425 .
•       Bib
  Peters, J.; Kober, J.; Muelling, K.; Nguyen-Tuong, D.; Kroemer, O. (2012). Robot Skill Learning, Proceedings of the European Conference on Artificial Intelligence (ECAI).
•     Bib
  Sigaud, O.; Peters, J. (2012). Robot Learning, Encyclopedia of the Sciences of Learning, Springer Verlag, Springer Verlag.
•       Bib
  van Hoof, H.; Kroemer, O.;Ben Amor, H.; Peters, J. (2012). Maximally Informative Interaction Learning for Scene Exploration, Proceedings of the International Conference on Robot Systems (IROS).
•       Bib
  Vitzthum, A.; Ben Amor, H.; Heumer, G.; Jung, B. (2012). XSAMPL3D - An Action Description Language for the Animation of Virtual Characters, Journal of Virtual Reality and Broadcasting, 9, 1.
•       Bib
  Wang, Z.;Deisenroth, M; Ben Amor, H.; Vogt, D.; Schoelkopf, B.; Peters, J. (2012). Probabilistic Modeling of Human Movements for Intention Inference, Proceedings of Robotics: Science and Systems (R:SS).

2011

•       Bib
  Bocsi, B.; Nguyen-Tuong, D; Csato, L; Schoelkopf, B.; Peters, J. (2011). Learning Inverse Kinematics with Structured Prediction, IEEE/RSJ International Conference on Intelligent Robot Systems (IROS).
•     Bib
  Boularias, A.; Kober, J.; Peters, J. (2011). Relative Entropy Inverse Reinforcement Learning, Proceedings of Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2011).
•       Bib
  Boularias, A.; Kroemer, O.; Peters, J. (2011). Learning Robot Grasping from 3D Images with Markov Random Fields, IEEE/RSJ International Conference on Intelligent Robot Systems (IROS).
•     Bib
  Detry, R.; Kraft, D.; Kroemer, O.; Peters, J.; Krueger, N.; Piater, J.; (2011). Learning Grasp Affordance Densities, Paladyn Journal of Behavioral Robotics, 2, 1, pp.1-17.
•       Bib
  Gomez Rodriguez, M.; Grosse-Wentrup, M.; Hill, J.; Schoelkopf, B.; Gharabaghi, A.; Peters, J. (2011). Towards Brain-Robot Interfaces for Stroke Rehabilitation, Proceedings of the International Conference on Rehabilitation Robotics (ICORR).
•     Bib
  Gomez Rodriguez, M.; Peters, J.; Hill, J.; Schoelkopf, B.; Gharabaghi, A.; Grosse-Wentrup, M. (2011). Closing the Sensorimotor Loop: Haptic Feedback Helps Decoding of Motor Imagery, Journal of Neuroengineering, 8, 3.
•       Bib
  Hachiya, H.; Peters, J.; Sugiyama, M. (2011). Reward Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning, Neural Computation, 23, 11.
•     Bib
  Kober, J.; Oztop, E.; Peters, J. (2011). Reinforcement Learning to adjust Robot Movements to New Situations, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Best Paper Track.
•       Bib
  Kober, J.; Peters, J. (2011). Policy Search for Motor Primitives in Robotics, Machine Learning (MLJ), 84, 1-2, pp.171-203.
•       Bib
  Kober, J.; Peters, J. (2011). Learning Elementary Movements Jointly with a Higher Level Task, IEEE/RSJ International Conference on Intelligent Robot Systems (IROS).
•     Bib
  Kroemer, O.; Lampert, C.H.; Peters, J. (2011). Learning Dynamic Tactile Sensing with Robust Vision-based Training, IEEE Transactions on Robotics (T-Ro), 27, 3, pp.545-557.
•     Bib
  Kroemer, O.; Peters, J. (2011). A Flexible Hybrid Framework for Modeling Complex Manipulation Tasks, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Kroemer, O.; Peters, J. (2011). Active Exploration for Robot Parameter Selection in Episodic Reinforcement Learning, Proceedings of the 2011 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (ADPRL).
•       Bib
  Kroemer, O.; Peters, J. (2011). A Non-Parametric Approach to Dynamic Programming, Advances in Neural Information Processing Systems 25 (NIPS/NeurIPS), MIT Press.
•     Bib
  Lampariello, R.; Nguyen Tuong, D.; Castellini, C.; Hirzinger, G.; Peters, J. (2011). Energy-optimal robot catching in real-time, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
•       Bib
  Muelling, K.; Kober, J.; Peters, J. (2011). A Biomimetic Approach to Robot Table Tennis, Adaptive Behavior Journal, 19, 5.
•     Bib
  Nguyen Tuong, D.; Peters, J. (2011). Incremental Sparsification for Real-time Online Model Learning, Neurocomputing, 74, 11, pp.1859-1867.
•       Bib
  Nguyen Tuong, D.; Peters, J. (2011). Learning Task-Space Tracking Control with Kernels, IEEE/RSJ International Conference on Intelligent Robot Systems (IROS).
•     Bib
  Nguyen Tuong, D.; Peters, J. (2011). Model Learning in Robotics: a Survey, Cognitive Processing, 12, 4.
•     Bib
  Piater, J.; Jodogne, S.; Detry, R.; Kraft, D.; Krueger, N.; Kroemer, O.; Peters, J. (2011). Learning Visual Representations for Perception-Action Systems, International Journal of Robotics Research (IJRR), 30, 3, pp.294-307.
•       Bib
  van Hoof, H.; van der Zant, T. ; Wiering, M.A. (2011). Adaptive Visual Face Tracking for an Autonomous Robot, Proceedings of the Belgian-Dutch Artificial Intelligence Conference (BNAIC 11).
•     Bib
  Wang, Z.; Boularias, A.; Muelling, K.; Peters, J. (2011). Balancing Safety and Exploitability in Opponent Modeling, Proceedings of the Twenty-Fifth National Conference on Artificial Intelligence (AAAI).
•       Bib
  Wang, Z.; Lampert, C; Muelling, K; Schoelkopf, B.; Peters, J. (2011). Learning Anticipation Policies for Robot Table Tennis, IEEE/RSJ International Conference on Intelligent Robot Systems (IROS).

2010

•     Bib
  Alvaro, M.; Peters, J.; Schoelfkopf, B.; Lawrence, N. (2010). Switched Latent Force Models for Movement Segmentation, Advances in Neural Information Processing Systems 24 (NIPS/NeurIPS), Cambridge, MA: MIT Press.
•     Bib
  Chiappa, S.; Peters, J. (2010). Movement extraction by detecting dynamics switches and repetitions, Advances in Neural Information Processing Systems 24 (NIPS/NeurIPS), Cambridge, MA: MIT Press.
•     Bib
  Detry, R.; Baseski, E.; Popovic, M.; Touati, Y.; Krueger, N.; Kroemer, O.; Peters, J.; Piater, J. (2010). Learning Continuous Grasp Affordances by Sensorimotor Exploration, From Motor Learning to Interaction Learning in Robots, Springer Verlag, 264.
•       Bib
  Erkan, A.: Kroemer, O.; Detry, R.; Altun, Y.; Piater, J.; Peters, J. (2010). Learning Probabilistic Discriminative Models of Grasp Affordances under Limited Supervision, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•     Bib
  Gomez Rodriguez, M.; Grosse Wentrup, M.; Peters, J.; Naros, G.; Hill, J.; Gharabaghi, A.; Schoelkopf, B. (2010). Epidural ECoG Online Decoding of Arm Movement Intention in Hemiparesis, 1st ICPR Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging.
•     Bib
  Gomez Rodriguez, M.; Peters, J.; Hill, J.; Gharabaghi, A.; Schoelkopf, B.; Grosse-Wentrup, M. (2010). BCI and robotics framework for stroke rehabilitation, Proceedings of the 4th International BCI Meeting, May 31 - June 4, 2010. Asilomar, CA, USA.
•     Bib
  Gomez Rodriguez, M.; Peters, J.; Hill, J.; Schoelkopf, B.; Gharabaghi, A.; Grosse-Wentrup, M. (2010). Closing the Sensorimotor Loop: Haptic Feedback Facilitates Decoding of Arm Movement Imagery, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (Workshop on Brain-Machine Interfaces).
•     Bib
  Kober, J.; Mohler, B.; Peters, J. (2010). Imitation and Reinforcement Learning for Motor Primitives with Perceptual Coupling, From Motor Learning to Interaction Learning in Robots, Springer Verlag.
•     Bib
  Kober, J.; Muelling, K.; Kroemer, O.; Lampert, C.H.; Schoelkopf, B.; Peters, J. (2010). Movement Templates for Learning of Hitting and Batting, IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Kober, J.; Oztop, E.; Peters, J. (2010). Reinforcement Learning to adjust Robot Movements to New Situations, Proceedings of Robotics: Science and Systems (R:SS).
•       Bib
  Kober, J.; Peters, J. (2010). Imitation and Reinforcement Learning - Practical Algorithms for Motor Primitive Learning in Robotics, IEEE Robotics and Automation Magazine, 17, 2, pp.55-62.
•       Bib
  Kroemer, O.; Detry, R.; Piater, J.; Peters, J. (2010). Adapting Preshaped Grasping Movements using Vision Descriptors, From Animals to Animats 11, International Conference on the Simulation of Adaptive Behavior (SAB).
•       Bib
  Kroemer, O.; Detry, R.; Piater, J.; Peters, J. (2010). Grasping with Vision Descriptors and Motor Primitives, Proceedings of the International Conference on Informatics in Control, Automation and Robotics (ICINCO).
•       Bib
  Kroemer, O.; Detry, R.; Piater, J.; Peters, J. (2010). Combining Active Learning and Reactive Control for Robot Grasping, Robotics and Autonomous Systems, 58, 9, pp.1105-1116.
•     Bib
  Lampert, C. H.; Kroemer, O. (2010). Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning, Proceedings of the 11th European Conference on Computer Vision (ECCV 2010).
•     Bib
  Morimura, T.; Uchibe, E.; Yoshimoto, J.; Peters, J.; Doya, K. (2010). Derivatives of Logarithmic Stationary Distributions for Policy Gradient Reinforcement Learning, Neural Computation, 22, 2.
•       Bib
  Muelling, K.; Kober, J.; Peters, J. (2010). Simulating Human Table Tennis with a Biomimetic Robot Setup, From Animals to Animats 11, International Conference on the Simulation of Adaptive Behavior (SAB).
•       Bib
  Muelling, K.; Kober, J.; Peters, J. (2010). A Biomimetic Approach to Robot Table Tennis, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
•       Bib
  Muelling, K.; Kober, J.; Peters, J. (2010). Learning Table Tennis with a Mixture of Motor Primitives, 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2010).
•     Bib
  Nguyen Tuong, D.; Peters, J. (2010). Incremental Sparsification for Real-time Online Model Learning, Proceedings of Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010).
•     Bib
  Nguyen Tuong, D.; Peters, J. (2010). Using Model Knowledge for Learning Inverse Dynamics, IEEE International Conference on Robotics and Automation.
•     Bib
  Nguyen Tuong, D.; Peters, J.;Seeger, M. (2010). Real-Time Local GP Model Learning, From Motor Learning to Interaction Learning in Robots, Springer Verlag, 264.
•     Bib
  Peters, J.; Bagnell, J.A. (2010). Policy gradient methods, Encyclopedia of Machine Learning (invited article).
•     Bib
  Peters, J.; Muelling, K.; Altun, Y. (2010). Relative Entropy Policy Search, Proceedings of the Twenty-Fourth National Conference on Artificial Intelligence (AAAI), Physically Grounded AI Track.
•   Bib
  Peters, J.; Muelling, K.; Kober, J. (2010). Experiments with Motor Primitives to learn Table Tennis, 12th International Symposium on Experimental Robotics (ISER 2010).
•     Bib
  Peters, J.; Tedrake, R.; Roy, N.; Morimoto, J. (2010). Robot Learning, Encyclopedia of Machine Learning.
•     Bib
  Peters, J.;Kober, J.;Schaal, S. (2010). Policy learning algorithmis for motor learning (Algorithmen zum automatischen Erlernen von Motorfaehigkigkeiten), Automatisierungstechnik, 58, 12, pp.688-694.
•     Bib
  Sehnke, F.; Osendorfer, C.; Rueckstiess, T.; Graves, A.; Peters, J.; Schmidhuber, J. (2010). Parameter-exploring Policy Gradients, Neural Networks, 23.
•     Bib
  Sigaud, O.; Peters, J. (2010). From Motor Learning to Interaction Learning in Robots, Studies in Computational Intelligence, Springer Verlag, 264.
•     Bib
  Wierstra, D.; Foerster, A.; Peters, J.; Schmidhuber, J. (2010). Recurrent Policy Gradients, Logic Journal of the IGPL, 18, pp.620-634.

2009

•     Bib
  Chiappa, S.; Kober, J.; Peters, J. (2009). Using Bayesian Dynamical Systems for Motion Template Libraries, Advances in Neural Information Processing Systems 22 (NIPS/NeurIPS), Cambridge, MA: MIT Press.
•     Bib
  Deisenroth, M.P.; Rasmussen, C.E.; Peters, J. (2009). Gaussian Process Dynamic Programming, Neurocomputing, 72, pp.1508-1524.
•     Bib
  Detry, R; Baseski, E.; Popovic, M.; Touati, Y.; Krueger, N; Kroemer, O.; Peters, J.; Piater, J; (2009). Learning Object-specific Grasp Affordance Densities, Proceedings of the International Conference on Development & Learning (ICDL 2009).
•       Bib
  Gomez Rodriguez, M.; Kober, J.; Schoelkopf, B. (2009). Denoising photographs using dark frames optimized by quadratic programming, Proceedings of the First IEEE International Conference on Computational Photography (ICCP 2009).
•     Bib
  Hachiya, H.; Akiyama, T.; Sugiyama, M.; Peters, J. (2009). Adaptive Importance Sampling for Value Function Approximation in Off-policy Reinforcement Learning, Neural Networks, 22, 10, pp.1399-1410.
•     Bib
  Hachiya, H.; Akiyama, T.; Sugiyama, M.; Peters, J. (2009). Efficient Data Reuse in Value Function Approximation, Proceedings of the 2009 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.
•     Bib
  Hachiya, H.; Peters, J.; Sugiyama, M. (2009). Efficient Sample Reuse in EM-based Policy Search, Proceedings of the 16th European Conference on Machine Learning (ECML 2009).
•     Bib
  Hoffman, M.; de Freitas, N. ; Doucet, A.; Peters, J. (2009). An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AIStats).
•     Bib
  Kober, J.; Peters, J. (2009). Learning new basic Movements for Robotics, Proceedings of Autonome Mobile Systeme (AMS 2009).
•     Bib
  Kober, J.; Peters, J. (2009). Policy Search for Motor Primitives in Robotics, Advances in Neural Information Processing Systems 22 (NIPS/NeurIPS), Cambridge, MA: MIT Press.
•     Bib
  Kober, J.; Peters, J. (2009). Learning Motor Primitives for Robotics, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
•     Bib
  Kober, J.; Peters, J. (2009). Reinforcement Learning fuer Motor-Primitive, Kuenstliche Intelligenz.
•     Bib
  Kroemer, O.; Detry, R.; Piater, J.; Peters, J. (2009). Active Learning Using Mean Shift Optimization for Robot Grasping, Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009).
•     Bib
  Lampert, C.H.; Peters, J. (2009). Active Structured Learning for High-Speed Object Detection, Proceedings of the DAGM (Pattern Recognition).
•     Bib
  Muelling, K.; Peters, J. (2009). A computational model of human table tennis for robot application, Proceedings of Autonome Mobile Systeme (AMS 2009).
•     Bib
  Neumann, G.; Maass, W; Peters, J. (2009). Learning Complex Motions by Sequencing Simpler Motion Templates, Proceedings of the International Conference on Machine Learning (ICML2009).
•     Bib
  Neumann, G.; Peters, J. (2009). Fitted Q-iteration by Advantage Weighted Regression, Advances in Neural Information Processing Systems 22 (NIPS/NeurIPS), Cambridge, MA: MIT Press.
•     Bib
  Nguyen Tuong, D.; Seeger, M.; Peters, J. (2009). Local Gaussian Process Regression for Real Time Online Model Learning and Control, Advances in Neural Information Processing Systems 22 (NIPS/NeurIPS), Cambridge, MA: MIT Press.
•     Bib
  Nguyen Tuong, D.; Seeger, M.; Peters, J. (2009). Sparse Online Model Learning for Robot Control with Support Vector Regression, Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009).
•     Bib
  Nguyen Tuong, D.; Seeger, M.; Peters, J. (2009). Model Learning with Local Gaussian Process Regression, Advanced Robotics, 23, 15, pp.2015-2034.
•     Bib
  Peters, J.; Kober, J. (2009). Using Reward-Weighted Imitation for Robot Reinforcement Learning, Proceedings of the 2009 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.
•     Bib
  Peters, J.; Kober, J.; Muelling, K.; Nguyen-Tuong, D.; Kroemer, O. (2009). Towards Motor Skill Learning for Robotics, Proceedings of the International Symposium on Robotics Research (ISRR), Invited Paper.
•     Bib
  Peters, J.; Morimoto, J.; Tedrake, R.; Roy, N. (2009). Robot Learning, IEEE Robotics & Automation Magazine, 16, 3, pp.19-20.
•     Bib
  Peters, J.; Ng, A. (2009). Guest Editorial: Special Issue on Robot Learning, Part B, Autonomous Robots (AURO), 27, 2.
•     Bib
  Peters, J.; Ng, A. (2009). Guest Editorial: Special Issue on Robot Learning, Part A, Autonomous Robots (AURO), 27, 1.
•     Bib
  Piater, J.; Jodogne, S.; Detry, R.; Kraft, D.; Krueger, N.; Kroemer, O.; Peters, J. (2009). Learning Visual Representations for Interactive Systems, Proceedings of the International Symposium on Robotics Research (ISRR), Invited Paper.
•     Bib
  Sigaud, O.; Peters, J. (2009). From Motor Learning to Interaction Learning in Robots, Proceedings of Journees Nationales de la Recherche en Robotique.

2008

•     Bib
  Deisenroth, M.P.; Peters, J.; Rasmussen, C.E. (2008). Approximate Dynamic Programming with Gaussian Processes, American Control Conference.
•     Bib
  Deisenroth, M.P.; Rasmussen, C.E.; Peters, J. (2008). Model-Based Reinforcement Learning with Continuous States and Actions, Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2008), pp.19-24.
•     Bib
  Hachiya, H.; Akiyama, T.; Sugiyama, M.; Peters, J. (2008). Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation, Proceedings of the Twenty-Third National Conference on Artificial Intelligence (AAAI 2008).
•       Bib
  Kober, J. (2008). Reinforcement Learning for Motor Primitives, Dipl-Ing Thesis, University of Stuttgart.
•     Bib
  Kober, J.; Mohler, B.; Peters, J. (2008). Learning Perceptual Coupling for Motor Primitives, International Conference on Intelligent Robot Systems (IROS).
•   Bib
  Kober, J.; Peters, J. (2008). Reinforcement Learning of Perceptual Coupling for Motor Primitives, Proceedings of the European Workshop on Reinforcement Learning (EWRL).
•   Bib
  Lesperance, Y.; Lakemeyer, G.; Peters, J.; Pirri, F. (2008). Proceedings of the 6th International Cognitive Robotics Workshop (CogRob 2008), July 21-22, 2008, Patras, Greece, ISBN 978-960-6843-09-9.
•     Bib
  Nakanishi, J.;Cory, R.;Mistry, M.;Peters, J.;Schaal, S. (2008). Operational space control: A theoretical and empirical comparison, International Journal of Robotics Research (IJRR), 27, 6, pp.737-757.
•     Bib
  Nguyen Tuong, D.; Peters, J. (2008). Learning Inverse Dynamics: a Comparison, Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2008).
•     Bib
  Nguyen Tuong, D.; Peters, J. (2008). Local Gaussian Processes Regression for Real-time Model-based Robot Control, International Conference on Intelligent Robot Systems (IROS).
•     Bib
  Nguyen Tuong, D.; Peters, J.; Seeger, M.; Schoelkopf, B. (2008). Learning Robot Dynamics for Computed Torque Control using Local Gaussian Processes Regression, Proceedings of the ECSIS Symposium on Learning and Adaptive Behavior in Robotic Systems, LAB-RS 2008.
•     Bib
  Peters, J. (2008). Machine Learning for Robotics, VDM-Verlag, ISBN 978-3-639-02110-3.
•     Bib
  Peters, J. (2008). Machine Learning for Motor Skills in Robotics, Kuenstliche Intelligenz, 3.
•     Bib
  Peters, J.; Kober, J.; Nguyen-Tuong, D. (2008). Policy Learning - a unified perspective with applications in robotics, Proceedings of the European Workshop on Reinforcement Learning (EWRL).
•     Bib
  Peters, J.; Nguyen-Tuong, D. (2008). Real-Time Learning of Resolved Velocity Control on a Mitsubishi PA-10, International Conference on Robotics and Automation (ICRA).
•     Bib
  Peters, J.; Schaal, S. (2008). Natural actor critic, Neurocomputing, 71, 7-9, pp.1180-1190.
•     Bib
  Peters, J.; Schaal, S. (2008). Learning to control in operational space, International Journal of Robotics Research (IJRR), 27, pp.197-212.
•     Bib
  Peters, J.; Schaal, S. (2008). Reinforcement learning of motor skills with policy gradients, Neural Networks, 21, 4, pp.682-97.
•     Bib
  Peters, J.; Seeger, M. (2008). Computed Torque Control with Nonparametric Regressions Techniques, American Control Conference.
•     Bib
  Peters, J.;Mistry, M.;Udwadia, F. E.;Nakanishi, J.;Schaal, S. (2008). A unifying framework for robot control with redundant DOFs, Autonomous Robots (AURO), 24, 1, pp.1-12.
•     Bib
  Sehnke, F.; Osendorfer, C; Rueckstiess, T; Graves, A.; Peters, J.; Schmidhuber, J. (2008). Policy Gradients with Parameter-based Exploration for Control, Proceedings of the International Conference on Artificial Neural Networks (ICANN).
•     Bib
  Steinke, F.; Hein, M.; Peters, J.; Schoelkopf, B (2008). Manifold-valued Thin-Plate Splines with Applications in Computer Graphics, Computer Graphics Forum (Special Issue on Eurographics 2008), 27, 2.
•     Bib
  Wierstra, D.; Schaul, T.; Peters, J.; Schmidthuber, J. (2008). Fitness Expectation Maximization, 10th International Conference on Parallel Problem Solving from Nature (PPSN 2008).
•     Bib
  Wierstra, D.; Schaul, T.; Peters, J.; Schmidhuber, J. (2008). Natural Evolution Strategies, 2008 IEEE Congress on Evolutionary Computation.
•     Bib
  Wierstra, D.; Schaul,T.; Peters, J.; Schmidhuber, J. (2008). Episodic Reinforcement Learning by Logistic Reward-Weighted Regression, Proceedings of the International Conference on Artificial Neural Networks (ICANN).

2007

•     Bib
  Nakanishi, J.; Mistry, M.; Peters, J.; Schaal, S. (2007). Experimental evaluation of task space position/orientation control towards compliant control for humanoid robots, IEEE International Conference on Intelligent Robotics Systems (IROS 2007).
•   Bib
  Peters, J. (2007). Computational Intelligence: By Amit Konar, The Computer Journal, 50, 6, pp.758.
•   Bib
  Peters, J. (2007). Machine Learning of Motor Skills for Robotics, Ph.D. Thesis, Department of Computer Science, University of Southern California.
•     Bib
  Peters, J. (2007). Relative Entropy Policy Search, CLMC Technical Report: TR-CLMC-2007-2, University of Southern California.
•     Bib
  Peters, J.; Schaal, S. (2007). Policy Learning for Motor Skills, Proceedings of 14th International Conference on Neural Information Processing (ICONIP).
•     Bib
  Peters, J.; Schaal, S. (2007). Reinforcement learning for operational space control, International Conference on Robotics and Automation (ICRA2007).
•     Bib
  Peters, J.; Schaal, S. (2007). Applying the episodic natural actor-critic architecture to motor primitive learning, Proceedings of the 2007 European Symposium on Artificial Neural Networks (ESANN).
•     Bib
  Peters, J.; Schaal, S.; Schoelkopf, B. (2007). Towards Machine Learning of Motor Skills, Proceedings of Autonome Mobile Systeme (AMS).
•     Bib
  Peters, J.;Schaal, S. (2007). Using reward-weighted regression for reinforcement learning of task space control, Proceedings of the 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.
•     Bib
  Peters, J.;Schaal, S. (2007). Reinforcement learning by reward-weighted regression for operational space control, Proceedings of the International Conference on Machine Learning (ICML2007).
•   Bib
  Peters, J.;Theodorou, E.;Schaal, S. (2007). Policy gradient methods for machine learning, INFORMS Conference of the Applied Probability Society.
•     Bib
  Riedmiller, M.;Peters, J.;Schaal, S. (2007). Evaluation of policy gradient methods and variants on the cart-pole benchmark, Proceedings of the 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.
•   Bib
  Theodorou, E; Peters, J.; Schaal, S. (2007). Reinforcement Learning for Optimal Control of Arm Movements, Abstracts of the 37st Meeting of the Society of Neuroscience.
•     Bib
  Wierstra, D.; Foerster, A.; Peters, J.; Schmidhuber, J. (2007). Solving Deep Memory POMDPs with Recurrent Policy Gradients, Proceedings of the International Conference on Artificial Neural Networks (ICANN).

2006

•     Bib
  Peters, J.;Schaal, S. (2006). Learning operational space control, Robotics: Science and Systems (RSS 2006), Cambridge, MA: MIT Press.
•     Bib
  Peters, J.;Schaal, S. (2006). Reinforcement Learning for Parameterized Motor Primitives, Proceedings of the 2006 International Joint Conference on Neural Networks (IJCNN).
•     Bib
  Peters, J.;Schaal, S. (2006). Policy gradient methods for robotics, Proceedings of the IEEE International Conference on Intelligent Robotics Systems (IROS 2006).
•     Bib
  Ting, J.;Mistry, M.;Nakanishi, J.;Peters, J.;Schaal, S. (2006). A Bayesian approach to nonlinear parameter identification for rigid body dynamics, Robotics: Science and Systems (RSS 2006), Cambridge, MA: MIT Press.

2005

•     Bib
  Nakanishi, J.;Cory, R.;Mistry, M.;Peters, J.;Schaal, S. (2005). Comparative experiments on task space control with redundancy resolution, IEEE International Conference on Intelligent Robots and Systems (IROS 2005).
•     Bib
  Peters, J.;Mistry, M.;Udwadia, F. E.;Cory, R.;Nakanishi, J.;Schaal, S. (2005). A unifying framework for the control of robotics systems, IEEE International Conference on Intelligent Robots and Systems (IROS 2005).
•     Bib
  Peters, J.;Mistry, M.;Udwadia, F. E.;Schaal, S. (2005). A new methodology for robot control design, The 5th ASME International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC 2005).
•     Bib
  Peters, J.;Vijayakumar, S.;Schaal, S. (2005). Natural Actor-Critic, Proceedings of the 16th European Conference on Machine Learning (ECML 2005).

2004

•   Bib
  Peters, J.; Schaal, S. (2004). Learning Motor Primitives with Reinforcement Learning, Proceedings of the 11th Joint Symposium on Neural Computation.
•     Bib
  Schaal, S.;Peters, J.;Nakanishi, J.;Ijspeert, A. (2004). Learning Movement Primitives, International Symposium on Robotics Research (ISRR2003).

2003

•     Bib
  Mohajerian, P.;Peters, J.;Ijspeert, A.;Schaal, S. (2003). A unifying computational framework for optimization and dynamic systems approaches to motor control, Proceedings of the 10th Joint Symposium on Neural Computation (JSNC 2003).
•     Bib
  Peters, J.;Vijayakumar, S.;Schaal, S. (2003). Reinforcement learning for humanoid robotics, IEEE-RAS International Conference on Humanoid Robots (Humanoids2003).
•     Bib
  Peters, J.;Vijayakumar, S.;Schaal, S. (2003). Scaling reinforcement learning paradigms for motor learning, Proceedings of the 10th Joint Symposium on Neural Computation (JSNC 2003).
•     Bib
  Schaal, S.;Peters, J.;Nakanishi, J.;Ijspeert, A. (2003). Control, planning, learning, and imitation with dynamic movement primitives, Workshop on Bilateral Paradigms on Humans and Humanoids, IEEE International Conference on Intelligent Robots and Systems (IROS 2003).

2002

•     Bib
  Peters, J. (2002). Policy Gradient Methods for Control Applications, CLMC Technical Report: TR-CLMC-2007-1, University of Southern California.
•   Bib
  Vijayakumar, S.; D’Souza, A.; Peters, J.; Conradt, J.; Rutkowski,T.; Ijspeert, A.; Nakanishi, J.; Inoue, M.; Shibata, T.; Wiryo, A.; Itti, L.; Amari, S.; Schaal, S (2002). Real-Time Statistical Learning for Oculomotor Control and Visuomotor Coordination, Advances in Neural Information Processing Systems (NIPS/NeurIPS), Demonstration Track.

2001

•       Bib
  Burdet, E.; Tee, K.P.; Chew, C.M.; Peters, J.; Bt, V.L. (2001). Hybrid IDM/Impedance Learning in Human Movements, First International Symposium on Measurement, Analysis and Modeling of Human Functions Proceedings.

2000

•       Bib
  Peters, J.; Riener, R (2000). A real-time model of the human knee for application in virtual orthopaedic trainer, Proceedings of the 10th International Conference on Biomedical Engineering Conference (ICBME).

1998

•   Bib
  Peters, J. (1998). Fuzzy Logic for Practical Applications, Kuenstliche Intelligenz (KI), 4, pp.60.