PointMapPolicy: Structured Point Cloud Processing for Multi-Modal Imitation Learning
Jia, X.; Wang, Q.; Wang, A.; Wang, H. A.; Gyenes, B.; Gospodinov, E.; Jiang, X.; Li, G.; Zhou, H.; Liao, W.; Huang, X.; Beck, M.; Reuss, M.; Lioutikov, R.; Neumann, G.
2025. The Thirty-ninth Annual Conference on Neural Information Processing Systems; San Diego, USA, 02.-07.12.2025, 30 S 

Point Cloud Segmentation for Autonomous Clip Positioning in Laparoscopic Cholecystectomy on a Phantom
Gyenes, B.; Franke, N.; Scheikl, P. M.; Henrich, P.; Younis, R.; Neumann, G.; Wagner, M.; Mathis-Ullrich, F.
2025. IEEE Robotics and automation letters, 10 (8), 8522–8529. doi:10.1109/LRA.2025.3585357

Towards Safe and Efficient Learning in the Wild: Guiding RL With Constrained Uncertainty-Aware Movement Primitives
Padalkar, A.; Stulp, F.; Neumann, G.; Silvério, J.
2025. IEEE Robotics and Automation Letters, 10 (7), 6880–6887. doi:10.1109/LRA.2025.3566599

Scaffolding Dexterous Manipulation with Vision-Language Models
Bakker, V. de; Hejna, J.; Lum, T. G. W.; Celik, O.; Taranovic, A.; Blessing, D.; Neumann, G.; Bohg, J.; Sadigh, D.
2025. The 39th Annual Conference on Neural Information Processing Systems; San Diego, USA, 02.-07.12.2025, 16 S 

Enhancing Exploration With Diffusion Policies in Hybrid Off-Policy RL: Application to Non-Prehensile Manipulation
Le, H.; Hoang, T.; Gabriel, M.; Neumann, G.; Vien, N. A.
2025. IEEE Robotics and Automation Letters, 10 (6), 6143–6150. doi:10.1109/LRA.2025.3564780

DIME: Diffusion-Based Maximum Entropy Reinforcement Learning
Celik, O.; Li, Z.; Blessing, D.; Li, G.; Palenicek, D.; Peters, J.; Chalvatzaki, G.; Neumann, G.
2025. Forty-second International Conference on Machine Learning; Vancouver, Kanada, 13.-19.07.2025., 20 S., ICML 

Diffusion for Multi-Embodiment Grasping
Freiberg, R.; Qualmann, A.; Vien, N. A.; Neumann, G.
2025. IEEE Robotics and Automation Letters, 10 (3), 2694–2701. doi:10.1109/LRA.2025.3534065

Towards Measuring Predictability: To which extent data-driven approaches can extract deterministic relations from data exemplified with time series prediction and classification
Zadeh, S. G.; Shaj, V.; Jahnke, P.; Neumann, G.; Breitenbach, T.
2025. Transactions on Machine Learning Research, 2025-February, 1–56

MaNGO Adaptable Graph Network Simulators via Meta-Learning
Dahlinger, P.; Hoang, T.; Blessing, D.; Freymuth, N.; Neumann, G.
2025. The 39th Annual Conference on Neural Information Processing Systems, 27 S 

Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inference
Blessing, D.; Berner, J.; Richter, L.; Domingo-Enrich, C.; Du, Y.; Vahdat, A.; Neumann, G.
2025. The 39th Annual Conference on Neural Information Processing Systems (NeurlPS 2025), 49 S 

AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction
Freymuth, N.; Würth, T.; Schreiber, N.; Gyenes, B.; Boltres, A.; Mitsch, J.; Taranovic, A.; Hoang, T.; Dahlinger, P.; Becker, P.; Kärger, L.; Neumann, G.
2025. The 39th Annual Conference on Neural Information Processing Systems (NeurlPS 2025), 39 S 

Extended neural contractive dynamical systems: On multiple tasks and Riemannian safety regions
Beik-Mohammadi, H.; Hauberg, S.; Arvanitidis, G.; Neumann, G.; Rozo, L.
2025. The International Journal of Robotics Research. doi:10.1177/02783649251366326

Identifying individual cost-balancing strategies when self-organizing task switching
Monno, I.; Dahlinger, P.; Miller, J.; Neumann, G.; Kiesel, A.
2025. Journal of Experimental Psychology: Human Perception and Performance, 51 (8), 1063–1084. doi:10.1037/xhp0001326

MaIL: Improving Imitation Learning with Selective State Space Models
Jia, X.; Wang, Q.; Donat, A.; Xing, B.; Li, G.; Zhou, H.; Celik, O.; Blessing, D.; Lioutikov, R.; Neumann, G.
2025. Proceedings of the 8th Conference on Robot Learning, 3888 – 3907, Machine Learning Research Press (ML Research Press)

TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning
Li, G.; Tian, D.; Zhou, H.; Jiang, X.; Lioutikov, R.; Neumann, G.
2025. The Thirteenth International Conference on Learning Representations, 60311–60340, International Conference on Learning Representations (ICLR)

Efficient Off-Policy Learning for High-Dimensional Action Spaces
Otto, F.; Becker, P.; Ngo, V. A.; Neumann, G.
2025. The Thirteenth International Conference on Learning Representations, 48469–48487, International Conference on Learning Representations (ICLR)

Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects
Hoang, T.; Le, H.; Becker, P.; Ngo, V. A.; Neumann, G.
2025. The Thirteenth International Conference on Learning Representations, 12588–12617, International Conference on Learning Representations (ICLR)

Underdamped Diffusion Bridges with Applications to Sampling
Blessing, D.; Berner, J.; Richter, L.; Neumann, G.
2025. The Thirteenth International Conference on Learning Representations, 69972–70004, International Conference on Learning Representations (ICLR)

End-to-end Learning of Gaussian Mixture Priors for Diffusion Sampler
Blessing, D.; Jia, X.; Neumann, G.
2025. The Thirteenth International Conference on Learning Representations, 47023–47049, International Conference on Learning Representations (ICLR)

Sequential Controlled Langevin Diffusions
Chen, J.; Richter, L.; Berner, J.; Blessing, D.; Neumann, G.; Anandkumar, A.
2025. The Thirteenth International Conference on Learning Representations, 28996–29030, International Conference on Learning Representations (ICLR)

The role of an ontology-based knowledge backbone in a circular factory
Hofmann, C.; Staab, S.; Selzer, M.; Neumann, G.; Furmans, K.; Heizmann, M.; Beyerer, J.; Lanza, G.; Pfrommer, J.; Düser, T.; Klein, J.-F.
2024. at - Automatisierungstechnik, 72 (9), 875–883. doi:10.1515/auto-2024-0006

Self-learning and autonomously adapting manufacturing equipment for the circular factory
Fleischer, J.; Zanger, F.; Schulze, V.; Neumann, G.; Stricker, N.; Furmans, K.; Pfrommer, J.; Lanza, G.; Hansjosten, M.; Fischmann, P.; Dvorak, J.; Klein, J.-F.; Rauscher, F.; Ebner, A.; May, M. C.; Gönnheimer, P.
2024. at - Automatisierungstechnik, 72 (9), 861–874. doi:10.1515/auto-2024-0005

Learning human actions from complex manipulation tasks and their transfer to robots in the circular factory = Erlernen menschlicher Handlungen aus komplexen Manipulationsaufgaben und deren Übertragung auf Roboter in einer Kreislauffabrik
Zaremski, M.; Handwerker, B.; Dreher, C. R. G.; Leven, F.; Schneider, D.; Roitberg, A.; Stiefelhagen, R.; Neumann, G.; Heizmann, M.; Asfour, T.; Deml, B.
2024. at - Automatisierungstechnik, 72 (9), 844–860. doi:10.1515/auto-2024-0008

Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes
Würth, T.; Freymuth, N.; Zimmerling, C.; Neumann, G.; Kärger, L.
2024. Computer Methods in Applied Mechanics and Engineering, 429, Artkl.Nr.: 117102. doi:10.1016/j.cma.2024.117102 

Lens Capsule Tearing in Cataract Surgery using Reinforcement Learning
Peter, R. C.; Peikert, S.; Haide, L.; Pham, D. X. V.; Chettaoui, T.; Tagliabue, E.; Scheikl, P. M.; Fauser, J.; Hillenbrand, M.; Neumann, G.; Mathis-Ullrich, F.
2024. 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, 13th-17th May 2024, 15501 – 15508, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA57147.2024.10611714

Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects
Scheikl, P. M.; Schreiber, N.; Haas, C.; Freymuth, N.; Neumann, G.; Lioutikov, R.; Mathis-Ullrich, F.
2024. IEEE Robotics and Automation Letters, 9 (6), 5338–5345. doi:10.1109/LRA.2024.3382529

A Comprehensive User Study on Augmented Reality-Based Data Collection Interfaces for Robot Learning
Jiang, X.; Mattes, P.; Jia, X.; Schreiber, N.; Neumann, G.; Lioutikov, R.
2024. HRI ’24: Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, 333 – 342, Association for Computing Machinery (ACM). doi:10.1145/3610977.3634995

Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning
Li, G.; Zhou, H.; Roth, D.; Thilges, S.; Otto, F.; Lioutikov, R.; Neumann, G.
2024. ICLR 2024 : The Twelfth International Conference on Learning Representations, Vienna, 7th-11th May 2024, International Conference on Learning Representations (ICLR)

A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics
Liu, P.; Günster, J.; Funk, N.; Gröger, S.; Chen, D.; Bou-Ammar, H.; Jankowski, J.; Marić, A.; Calinon, S.; Orsula, A.; Olivares-Mendez, M.; Zhou, H.; Lioutikov, R.; Neumann, G.; Likmeta, A.; Zhalehmehrabi, A.; Bonenfant, T.; Restelli, M.; Tateo, D.; Liu, Z.; Peters, J.
2024. Advances in Neural Information Processing Systems (NeurIPS 2024), Ed.: A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, C. Zhang, 9690–9726, Curran Associates, Inc

Variational Distillation of Diffusion Policies into Mixture of Experts
Zhou, H.; Blessing, D.; Li, G.; Celik, O.; Jia, X.; Neumann, G.; Lioutikov, R.
2024. 38th Conference on Neural Information Processing Systems (NeurIPS 2024), Ed.: A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, C. Zhang, Curran Associates, Inc

PointPatchRL - Masked Reconstruction Improves Reinforcement Learning on Point Clouds
Gyenes, B.; Franke, N.; Becker, P.; Neumann, G.
2024. Proceedings of the 8th Conference on Robot Learning, 5236 – 5253, Machine Learning Research Press (ML Research Press)

Domain-Specific Fine-Tuning of Large Language Models for Interactive Robot Programming
Alt, B.; Keßner, U.; Taranovic, A.; Katic, D.; Hermann, A.; Jäkel, R.; Neumann, G.
2024. European Robotics Forum 2024 – 15th ERF, Volume 1. Ed.: C. Secchi, 274–279, Springer Nature Switzerland. doi:10.1007/978-3-031-76424-0_49

Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts
Celik, O.; Taranovic, A.; Neumann, G.
2024. Proceedings of the 41st International Conference on Machine Learning. Ed.: R. Salakhutdinov, 5907–5933, PMLR

Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning
Hüttenrauch, M.; Neumann, G.
2024. Journal of machine learning research, 25, 153:1–153:44 

Neural Contractive Dynamical Systems
Beik-Mohammadi, H.; Hauberg, S.; Arvanitidis, G.; Figueroa, N.; Neumann, G.; Rozo, L.
2024. The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024, OpenReview.net

Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling
Blessing, D.; Jia, X.; Esslinger, J.; Vargas, F.; Neumann, G.
2024. Proceedings of the 41st International Conference on Machine Learning. Ed.: R. Salakhutdinov, 4205–4229, PMLR

Towards Diverse Behaviors: A Benchmark for Imitation Learning with Human Demonstrations
Jia, X.; Blessing, D.; Jiang, X.; Reuss, M.; Donat, A.; Lioutikov, R.; Neumann, G.
2024. The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024, OpenReview.net

Swarm reinforcement learning for adaptive mesh refinement
Freymuth, N.; Dahlinger, P.; Würth, T.; Reisch, S.; Kärger, L.; Neumann, G.
2024. Advances in Neural Information Processing Systems, 36 S

Registered and Segmented Deformable Object Reconstruction from a Single View Point Cloud
Henrich, P.; Gyenes, B.; Scheikl, P. M.; Neumann, G.; Mathis-Ullrich, F.
2024. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 3129–3138

Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift
Seligmann, F.; Becker, P.; Volpp, M.; Neumann, G.
2024. Advances in Neural Information Processing Systems. Ed.: A. Oh, MIT-Press 

Information Maximizing Curriculum: A Curriculum-Based Approach for Learning Versatile Skills
Blessing, D.; Celik, O.; Jia, X.; Reuss, M.; Li, M.; Lioutikov, R.; Neumann, G.
2024. Advances in Neural Information Processing Systems. Ed.: A. Oh, MIT-Press

SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D Object Pose Estimation
Duffhauss, F.; Koch, S.; Ziesche, H.; Vien, N. A.; Neumann, G.
2023. IEEE Robotics and Automation Letters, 8 (9), 5315–5322. doi:10.1109/LRA.2023.3293317

Multi Time Scale World Models
Shaj, V.; Zadeh, S. G.; Demir, O.; Douat, L. R.; Neumann, G.
2023. Advances in neural information processing systems 36 : 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 10-16 December 2023, New Orleans, Louisana, USA. Ed.: A. Oh

A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models
Arenz, O.; Dahlinger, P.; Ye, Z.; Volpp, M.; Neumann, G.
2023. Transactions on machine learning research, 2023 

Grounding Graph Network Simulators using Physical Sensor Observations
Linkerhägner, J.; Freymuth, N.; Scheikl, P. M.; Mathis-Ullrich, F.; Neumann, G.
2023. 11th International Conference on Learning Representations (ICLR 2023), 27 S. doi:10.48550/arXiv.2302.11864 

Reinforcement Learning of Diverse Skills using Mixture of Deep Experts
Celik, O.; Taranovic, A.; Neumann, G.
2023. Intrinsically-Motivated and Open-Ended Learning Workshop@ NeurIPS2023

Reactive motion generation on learned Riemannian manifolds
Beik-Mohammadi, H.; Hauberg, S.; Arvanitidis, G.; Neumann, G.; Rozo, L.
2023. The International Journal of Robotics Research, 42 (10), 729–754. doi:10.1177/02783649231193046

An Encoder-Decoder Architecture for Smooth Motion Generation
Lončarević, Z.; Li, G.; Neumann, G.; Gams, A.
2023. Advances in Service and Industrial Robotics – RAAD 2023. Ed.: T. Petrič, 358 – 366, Springer Nature Switzerland. doi:10.1007/978-3-031-32606-6_42

ProDMP: A Unified Perspective on Dynamic and Probabilistic Movement Primitives
Li, G.; Jin, Z.; Volpp, M.; Otto, F.; Lioutikov, R.; Neumann, G.
2023. IEEE Robotics and automation letters, 1–8. doi:10.1109/LRA.2023.3248443

Adversarial Imitation Learning with Preferences
Taranovic, A.; Kupcsik, A. G.; Freymuth, N.; Neumann, G.
2023. The eleventh International Conference on Learning Representations : Kigali, Rwanda, Mon May 1-Fri May 5 

Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference
Volpp, M.; Dahlinger, P.; Becker, P.; Daniel, C.; Neumann, G.
2023. International Conference on Learning Representations 

On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning
Becker, P.; Neumann, G.
2022. Transactions on Machine Learning Research, (10) 

Human-Machine Symbiosis: A Multivariate Perspective for Physically Coupled Human-Machine Systems
Inga, J.; Ruess, M.; Robens, J. H.; Nelius, T.; Rothfuß, S.; Kille, S.; Dahlinger, P.; Lindenmann, A.; Thomaschke, R.; Neumann, G.; Matthiesen, S.; Hohmann, S.; Kiesel, A.
2022. International Journal of Human-Computer Studies, 170, Article no: 102926. doi:10.1016/j.ijhcs.2022.102926

Specializing versatile skill libraries using local mixture of experts
Celik, O.; Zhou, D.; Li, G.; Becker, P.; Neumann, G.
2022. Proceedings of the 5th Conference on Robot Learning. Ed.: A. Faust, 1423–1433

Deep Black-Box Reinforcement Learning with Movement Primitives
Otto, F.; Celik, O.; Zhou, H.; Ziesche, H.; Ngo, V. A.; Neumann, G.
2022. 6th Annual Conference on Robot Learning (CoRL 2022), 1244–1265, Machine Learning Research Press (ML Research Press) 

MV6D: Multi-View 6D Pose Estimation on RGB-D Frames Using a Deep Point-wise Voting Network
Duffhauss, F.; Demmler, T.; Neumann, G.
2022. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3568–3575, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IROS47612.2022.9982268

FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion
Duffhauss, F.; Vien, N. A.; Ziesche, H.; Neumann, G.
2022. Computer Vision – ECCV 2022 – 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXIX. Ed.: S. Avidan, 674–691, Springer Nature Switzerland. doi:10.1007/978-3-031-19842-7_39

Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptors
Freymuth, N.; Schreiber, N.; Taranovic, A.; Becker, P.; Neumann, G.
2022. 6th Conference on Robot Learning (CoRL 2022), Machine Learning Research Press (ML Research Press) 

Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
Shaj, V.; Büchler, D.; Sonker, R.; Becker, P.; Neumann, G.
2022. 10th International Conference on Learning Representations (ICLR), 23 S 

What Matters for Meta-Learning Vision Regression Tasks?
Gao, N.; Ziesche, H.; Vien, N. A.; Volpp, M.; Neumann, G.
2022. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14776–14786 

End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control
Reuss, M.; Duijkeren, N. van; Krug, R.; Becker, P.; Shaj, V.; Neumann, G.
2022. Robotics: Science and Systems XVIII. Ed.: K. Hauser. doi:10.48550/arXiv.2205.13804 

Hierarchical Policy Learning for Mechanical Search
Zenkri, O.; Vien, N. A.; Neumann, G.
2022. IEEE International Conference on Robotics and Automation (ICRA). doi:10.1109/ICRA46639.2022.9811572 

Push-to-See: Learning Non-Prehensile Manipulation to Enhance Instance Segmentation via Deep Q-Learning
Serhan, B.; Pandya, H.; Kucukyilmaz, A.; Neumann, G.
2022. IEEE International Conference on Robotics and Automation (ICRA 2022), 1513–1519, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA46639.2022.9811645 

Versatile Inverse Reinforcement Learning via Cumulative Rewards
Freymuth, N.; Becker, P.; Neumann, G.
2021. NeurIPS 2021 Workshop on Robot Learning: Self-Supervised and Lifelong Learning, Virtual 

Navigate-and-Seek: A Robotics Framework for People Localization in Agricultural Environments
Polvara, R.; Del Duchetto, F.; Neumann, G.; Hanheide, M.
2021. IEEE Robotics and automation letters, 6 (4), 6577–6584. doi:10.1109/LRA.2021.3094557

Coordinate ascent MORE with adaptive entropy control for population-based regret minimization
Hüttenrauch, M.; Neumann, G.
2021. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1493–1497, Association for Computing Machinery (ACM). doi:10.1145/3449726.3463183

Switching Recurrent Kalman Networks
Nguyen-Quynh, G.; Becker, P.; Qiu, C.; Rudolph, M.; Neumann, G.
2021. 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Curran Associates, Inc 

Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty
Ranjbar, A.; Vien, N. A.; Ziesche, H.; Boedecker, J.; Neumann, G.
2021. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2383–2390. doi:10.1109/IROS51168.2021.9636176

Cooperative Assistance in Robotic Surgery through Multi-Agent Reinforcement Learning
Scheikl, P. M.; Gyenes, B.; Davitashvili, T.; Younis, R.; Schulze, A.; Müller-Stich, B. P.; Neumann, G.; Wagner, M.; Mathis-Ullrich, F.
2021. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 27 Sept.-1 Oct. 2021, 1859–1864, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IROS51168.2021.9636193

A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning
Mohtasib, A.; Neumann, G.; Cuayáhuitl, H.
2021. Towards Autonomous Robotic Systems: 22nd Annual Conference, TAROS 2021, Lincoln, UK, September 8–10, 2021, Proceedings. Ed.: C. Fox, 3–13, Springer-Verlag. doi:10.1007/978-3-030-89177-0_1

Specializing Versatile Skill Libraries using Local Mixture of Experts
Celik, O.; Zhou, D.; Li, G.; Becker, P.; Neumann, G.
2021. 5th Annual Conference on Robot Learning. doi:10.48550/arXiv.2112.04216

Intent-Aware Predictive Haptic Guidance and its Application to Shared Control Teleoperation
Ly, K. T.; Poozhiyil, M.; Pandya, H.; Neumann, G.; Küçükyilmaz, A.
2021. 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), Vancouver, BC, Canada, 8-12 Aug. 2021, 565–572, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/RO-MAN50785.2021.9515326

Learning Riemannian Manifolds for Geodesic Motion Skills
Beik-Mohammadi, H.; Hauberg, S.; Arvanitidis, G.; Neumann, G.; Rozo, L.
2021. Robotics: Science and System XVII, July 12 - July 16, 2021 --Held Virtually--, Ed.: D. A. Shell. doi:10.15607/RSS.2021.XVII.082

Deep reinforcement learning for attacking wireless sensor networks
Parras, J.; Hüttenrauch, M.; Zazo, S.; Neumann, G.
2021. Sensors, 21 (12), 4060. doi:10.3390/s21124060 

Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation
Ashith Shyam, R. B.; Hao, Z.; Montanaro, U.; Dixit, S.; Rathinam, A.; Gao, Y.; Neumann, G.; Fallah, S.
2021. Frontiers in Robotics and AI, 8, Article: 638849. doi:10.3389/frobt.2021.638849 

Differentiable Trust Region Layers for Deep Reinforcement Learning
Fabian, O.; Becker, P.; Ngo, V.; Ziesche, H.; Neumann, G.
2021. International Conference on Learning Representations

Bayesian Context Aggregation for Neural Processes
Volpp, M.; Grossberger, L.; Daniel, C.; Flürenbrock, F.; Neumann, G.
2021. International Conference on Learning Representations

Probabilistic Approach to Physical Object Disentangling
Pajarinen, J.; Arenz, O.; Peters, J.; Neumann, G.
2020. IEEE Robotics and automation letters, 5 (4), 5510–5517. doi:10.1109/LRA.2020.3006789 

Trust-Region Variational Inference with Gaussian Mixture Models
Arenz, O.; Zhong, M.; Neumann, G.
2020. Journal of machine learning research, 21, 1–60 

Next-Best-Sense: a multi-criteria robotic exploration strategy for RFID tags discovery
Polvara, R.; Fernandez-Carmona, M.; Neumann, G.; Hanheide, M.
2020. IEEE Robotics and automation letters, 5 (3), 4477–4484. doi:10.1109/LRA.2020.3001539

A Haptic Shared-Control Architecture for Guided Multi-Target Robotic Grasping
Abi-Farraj, F.; Pacchierotti, C.; Arenz, O.; Neumann, G.; Giordano, P. R.
2020. IEEE transactions on haptics, 13 (2), 270–285. doi:10.1109/TOH.2019.2913643

Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning
Shaj, V.; Becker, P.; Buchler, D.; Pandya, H.; Duijkeren, N. van; Taylor, C. J.; Hanheide, M.; Neumann, G.
2020. Conference on Robot Learning (CoRL 2020) 

Adaptation and Robust Learning of Probabilistic Movement Primitives
Gomez-Gonzalez, S.; Neumann, G.; Schölkopf, B.; Peters, J.
2020. IEEE transactions on robotics, 36 (2), 366–379

Improving Local Trajectory Optimisation using Probabilistic Movement Primitives
Shyam, R. B. A.; Lightbody, P.; Das, G.; Liu, P.; Gomez-Gonzalez, S.; Neumann, G.
2020. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2666–2671, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IROS40897.2019.8967980

Haptic-Guided Shared Control Grasping for Collision-Free Manipulation
Parsa, S.; Kamale, D.; Mghames, S.; Nazari, K.; Pardi, T.; Srinivasan, A. R.; Neumann, G.; Hanhaide, M.; Ghalamzan, A.
2020. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CASE48305.2020.9216789

Expected Information Maximization: Using the I-Projection for Mixture Density Estimation
Becker, P.; Arenz, O.; Neumann, G.
2020. 8th International Conference on Learning Representations (ICLR 2020) 

Haptic-Guided Teleoperation of a 7-DoF Collaborative Robot Arm with an Identical Twin Master
Singh, J.; Srinivasan, A. R.; Neumann, G.; Kucukyilmaz, A.
2020. IEEE transactions on haptics, 13 (1), 246–252. doi:10.1109/TOH.2020.2971485

Sim-to-Real quadrotor landing via sequential deep Q-Networks and domain randomization
Polvara, R.; Patacchiola, M.; Hanheide, M.; Neumann, G.
2020. Robotics, 9 (1), 8. doi:10.3390/robotics9010008 

Grasping Unknown Objects Based on Gripper Workspace Spheres
Sorour, M.; Elgeneidy, K.; Srinivasan, A.; Hanheide, M.; Neumann, G.
2019. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019, Macau, SAR, China, November 3-8, 2019, 1541–1547, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IROS40897.2019.8967989

Learning Kalman Network: A deep monocular visual odometry for on-road driving
Zhao, C.; Sun, L.; Yan, Z.; Neumann, G.; Duckett, T.; Stolkin, R.
2019. Robotics and autonomous systems, 121. doi:10.1016/j.robot.2019.07.004

The kernel Kalman rule - Efficient nonparametric inference by recursive least-squares and subspace projections
Gebhardt, G. H. W.; Kupcsik, A. G.; Neumann, G.
2019. Machine learning, 108 (12), 2113–2157. doi:10.1007/s10994-019-05816-z

Compatible natural gradient policy search
Pajarinen, J.; Thai, H. L.; Akrour, R.; Peters, J.; Neumann, G.
2019. Machine learning, 108, 1443–1466. doi:10.1007/s10994-019-05807-0

Deep Reinforcement Learning for Swarm Systems
Hüttenrauch, M.; Adrian, S.; Neumann, G.
2019. Journal of machine learning research, 20 (54), 1–31 

Learning Replanning Policies with Direct Policy Search
Brandherm, F.; Peters, J.; Neumann, G.; Akrour, R.
2019. IEEE Robotics and automation letters, 4 (2), 2196 –2203. doi:10.1109/LRA.2019.2901656 

Characterising 3D-printed Soft Fin Ray Robotic Fingers with Layer Jamming Capability for Delicate Grasping
Elgeneidy, K.; Lightbody, P.; Pearson, S.; Neumann, G.
2019. 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft), 14-18 April 2019, Seoul, South Korea, 143–148, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ROBOSOFT.2019.8722715

Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
Becker, P.; Pandya, H.; Gebhardt, G.; Zhao, C.; Taylor, C. J.; Neumann, G.
2019. Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, CA, USA, 544–552 

Projections for Approximate Policy Iteration Algorithms
Akrour, R.; Pajarinen, J.; Neumann, G.; Peters, J.
2019. Proceedings of the International Conference on Machine Learning (ICML), 9th - 15th June 2019, Long Beach, CA, USA, 181–190

Model-Free Trajectory-based Policy Optimization with Monotonic Improvement
Akrour, R.; Abdolmaleki, A.; Abdulsamad, H.; Peters, J.; Neumann, G.
2018. Journal of machine learning research, 19 (14), 1–25 

Energy-efficient design and control of a vibro-driven robot
Liu, P.; Neumann, G.; Fu, Q.; Pearson, S.; Yu, H.
2018. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1-5 October 2018, Madrid, Spain, 1464–1469, Institute of Electrical and Electronics Engineers (IEEE)

Efficient Gradient-Free Variational Inference using Policy Search
Arenz, O.; Zhong, M.; Neumann, G.
2018. Proceedings of the 35th International Conference on Machine Learning, 10-15 July 2018, Stockholm, Sweden

Directly Printable Flexible Strain Sensors for Bending and Contact Feedback of Soft Actuators
Elgeneidy, K.; Neumann, G.; Jackson, M.; Lohse, N.
2018. Frontiers in robotics and AI, 5, Art.-Nr.: 2. doi:10.3389/frobt.2018.00002 

Contact Detection and Size Estimation using a Modular Soft Gripper with Embedded Flex Sensors
Elgeneidy, K.; Neumann, G.; Pearson, S.; Jackson, M.; Lohse, N.
2018. 2018 IEEE/RSJ International Conferenceon Intelligent Robots and Systems, IROS 2018, October, 1-5, 2018, Madrid, Spain, 498–503, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IROS.2018.8593399

Using probabilistic movement primitives in robotics
Paraschos, A.; Daniel, C.; Peters, J.; Neumann, G.
2018. Autonomous robots, 42 (3), 529–551. doi:10.1007/s10514-017-9648-7

Learning robust policies for object manipulation with robot swarms
Gebhardt, G. H. W.; Daun, K.; Schnaubelt, M.; Neumann, G.
2018. 2018 IEEE International Conference on Robotics and Automation (ICRA), 21-25 May 2018, Brisbane, QLD, Australia, 7688–7695, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA.2018.8463215

Learning coupled forward-inverse models with combined prediction errors
Koert, D.; Maeda, G.; Neumann, G.; Peters, J.
2018. 2018 IEEE International Conference on Robotics and Automation (ICRA), 21-25 May 2018, Brisbane, QLD, Australia, 2433–2439, Institute of Electrical and Electronics Engineers (IEEE)

Towards real-time robotic motion planning for grasping in cluttered and uncertain environments
Liu, P.; Elgeneidy, K.; Pearson, S.; Huda, N.; Neumann, G.
2018. 19th Towards Autonomous Robotic Systems (TAROS) Conference, 25-27 July 2018, Bristol, UK, 481–483, Springer

Printable Soft Grippers with Integrated Bend Sensing for Handling of Crops
Elgeneidy, K.; Liu, P.; Pearson, S.; Lohse, N.; Neumann, G.
2018. Towards Autonomous Robotic Systems: 19th Annual Conference, TAROS 2018, Bristol, UK July 25-27, 2018, Proceedings. Ed.: M. Giuliani, 479–480, Springer. doi:10.1007/978-3-319-96728-8

Hierarchical Reinforcement Learning of Multiple Grasping Strategies with Human Instructions
Osa, T.; Peters, J.; Neumann, G.
2018. Advanced robotics, 32 (18), 955–968. doi:10.1080/01691864.2018.1509018

Sample and feedback efficient hierarchical reinforcement learning from human preferences
Pinsler, R.; Akrour, R.; Osa, T.; Peters, J.; Neumann, G.
2018. 2018 IEEE International Conference on Robotics and Automation (ICRA), 21-25 May 2018, Brisbane, QLD, Australia, 596–601, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA.2018.8460907

Guiding trajectory optimization by demonstrated distributions
Osa, T.; Esfahani, A. M. G.; Stolkin, R.; Lioutikov, R.; Peters, J.; Neumann, G.
2017. IEEE Robotics and automation letters, 2 (2), 819–826. doi:10.1109/LRA.2017.2653850

The kernel Kalman rule: efficient nonparametric inference with recursive least squares
Gebhardt, G. H. W.; Kupcsik, A.; Neumann, G.
2017. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), 4-10 February 2017, San Francisco, CA, USA, 3754–3760, AAAI Press

Policy search with high-dimensional context variables
Tangkaratt, V.; Hoof, H. van; Parisi, S.; Neumann, G.; Peters, J.; Sugiyama, M.
2017. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), 4-10 February 2017, San Francisco, CA, USA, AAAI Press

Probabilistic movement primitives for coordination of multiple human-robot collaborative tasks
Maeda, G. J.; Neumann, G.; Ewerton, M.; Lioutikov, R.; Kroemer, O.; Peters, J.
2017. Autonomous robots, 41 (3), 593–612. doi:10.1007/s10514-016-9556-2

A learning-based shared control architecture for interactive task execution
Farraj, F. B.; Osa, T.; Pedemonte, N.; Peters, J.; Neumann, G.; Giordano, P. R.
2017. 2017 IEEE International Conference on Robotics and Automation (ICRA), 29 May - 3 June 2017, Singapore, 329–335, Institute of Electrical and Electronics Engineers (IEEE)

Layered direct policy search for learning hierarchical skills
End, F.; Akrour, R.; Peters, J.; Neumann, G.
2017. 2017 IEEE International Conference on Robotics and Automation (ICRA), 29 May - 3 June 2017, Singapore, 6442–6448, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA.2017.7989761

Empowered skills
Gabriel, A.; Akrour, R.; Peters, J.; Neumann, G.
2017. 2017 IEEE International Conference on Robotics and Automation (ICRA), 29 May - 3 June 2017, Singapore, 6435–6441, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA.2017.7989760

Learning to assemble objects with a robot swarm
Gebhardt, G. H. W.; Daun, K.; Schnaubelt, M.; Hendrich, A.; Kauth, D.; Neumann, G.
2017. 16th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2017, 8-12 May 2017, Sao Paulo, Brazil, 1547–1549, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)

Model-based contextual policy search for data-efficient generalization of robot skills
Kupcsik, A.; Deisenroth, M. P.; Peters, J.; Loh, A. P.; Vadakkepat, P.; Neumann, G.
2017. Artificial intelligence, 247, 415–439. doi:10.1016/j.artint.2014.11.005

State-regularized policy search for linearized dynamical systems
Abdulsamad, H.; Arenz, O.; Peters, J.; Neumann, G.
2017. Proceedings International Conference on Automated Planning and Scheduling, ICAPS 2017, 18-23 June 2017, Pittsburgh, PA, USA. Ed.: L. Barbulescu, 419–424, AAAI Press

Learning movement primitive libraries through probabilistic segmentation
Lioutikov, R.; Neumann, G.; Maeda, G.; Peters, J.
2017. International journal of robotics research, 36 (8), 879–894. doi:10.1177/0278364917713116

Probabilistic prioritization of movement primitives
Paraschos, A.; Lioutikov, R.; Peters, J.; Neumann, G.
2017. IEEE Robotics and automation letters, PP (99), 2294–2301. doi:10.1109/LRA.2017.2725440

Deriving and improving CMA-ES with Information geometric trust regions
Abdolmaleki, A.; Price, B.; Lau, N.; Reis, L. P.; Neumann, G.
2017. The Genetic and Evolutionary Computation Conference (GECCO 2017), July 15th - 19th 2017, Berlin, 657–664, Association for Computing Machinery (ACM). doi:10.1145/3071178.3071252

Local Bayesian optimization of motor skills
Akrour, R.; Sorokin, D.; Peters, J.; Neumann, G.
2017. Proceedings of the 34th International Conference on Machine Learning, ICML 2017, 6-11 August 2017, Sydney, Australia, 59–68, International Machine Learning Society (IMLS)

Contextual Covariance Matrix Adaptation Evolutionary Strategies
Abdolmaleki, A.; Price, B.; Lau, N.; Reis, P.; Neumann, G.
2017. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI), 22 - 25 August 2017, Melbourne, Australia, 1378–1385, IJCAI. doi:10.24963/ijcai.2017/191

Non-parametric policy search with limited information loss
Hoof, H. van; Neumann, G.; Peters, J.
2017. Journal of machine learning research, 18 (73), 1–46 

Hybrid control trajectory optimization under uncertainty
Pajarinen, J.; Kyrki, V.; Koval, M.; Srinivasa, S.; Peters, J.; Neumann, G.
2017. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 24-28 September 2017, Vancouver, BC, Canada, 5694–5701, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IROS.2017.8206460

A survey of preference-based reinforcement learning methods
Wirth, C.; Akrour, R.; Neumann, G.; Fürnkranz, J.
2017. Journal of machine learning research, 18 (136), 1–46 

Phase estimation for fast action recognition and trajectory generation in human-robot collaboration
Maeda, G.; Ewerton, M.; Neumann, G.; Lioutikov, R.; Peters, J.
2017. International journal of robotics research, 36 (13-14), 1579–1594. doi:10.1177/0278364917693927

Model-based relative entropy stochastic search
Abdolmaleki, A.; Lioutikov, R.; Lua, N.; Reis, L. P.; Peters, J.; Neumann, G.
2016. GECCO ’16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. Ed.: T. Friedrich, 153–154, Association for Computing Machinery (ACM). doi:10.1145/2908961.2930952

Model-free preference-based reinforcement learning
Wirth, C.; Furnkranz, J.; Neumann, G.
2016. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), 12-17 February 2016, Phoenix, AZ, USA, 2222–2228, AAAI Press

Experiments with hierarchical reinforcement learning of multiple grasping policies
Osa, T.; Peters, J.; Neumann, G.
2016. Proceedings of the International Symposium on Experimental Robotics (ISER), 3 - 6 October 2016, Tokyo, Japan

Learning soft task priorities for control of redundant robots
Modugno, V.; Neumann, G.; Rueckert, E.; Oriolo, G.; Peters, J.; Ivaldi, S.
2016. 2016 IEEE International Conference on Robotics and Automation (ICRA), 16-21 May 2016, Stockholm, Sweden, 221–226, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA.2016.7487137

Hierarchical relative entropy policy search
Daniel, C.; Neumann, G.; Kroemer, O.; Peters, J.
2016. Journal of machine learning research, 17, 1–50

Movement primitives with multiple phase parameters
Ewerton, M.; Maeda, G.; Neumann, G.; Kisner, V.; Kollegger, G.; Wiemeyer, J.; Peters, J.
2016. 2016 IEEE International Conference on Robotics and Automation (ICRA), 16-21 May 2016, Stockholm, Sweden, 201–206, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA.2016.7487134

Model-free trajectory optimization for reinforcement learning
Akrour, R.; Abdolmaleki, A.; Abdulsamad, H.; Neumann, G.
2016. Proceedings of The 33rd International Conference on Machine Learning, ICML 2016, 19-24 June 2016, New York City, NY, USA, 4342–4352, International Machine Learning Society (IMLS)

Contextual stochastic search
Abdolmaleki, A.; Lau, N.; Reis, L. P.; Neumann, G.
2016. Genetic and Evolutionary Computation Conference GECCO 2016, July 20-24 2016, Denver, CO, USA, 29–30, Association for Computing Machinery (ACM). doi:10.1145/2908961.2909012

Contextual policy search for linear and nonlinear generalization of a humanoid walking controller
Abdolmaleki, A.; Lau, N.; Reis, L. P.; Peters, J.; Neumann, G.
2016. Journal of intelligent and robotic systems, 83 (3), 393–408. doi:10.1007/s10846-016-0347-y

Probabilistic inference for determining options in reinforcement learning
Daniel, C.; Hoof, H. van; Peters, J.; Neumann, G.
2016. Machine learning, 104 (2-3), 337–357. doi:10.1007/s10994-016-5580-x

Optimal control and inverse optimal control by distribution matching
Arenz, O.; Abdulsamad, H.; Neumann, G.
2016. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, 9-14 October 2016, Daejeon, South Korea, 4046–4053, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IROS.2016.7759596

Non-parametric contextual stochastic search
Abdolmaleki, A.; Lau, N.; Reis, L. P.; Neumann, G.
2016. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 9-14 October 2016, Daejeon, South Korea, 2643–2648, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IROS.2016.7759411

A probabilistic approach to robot trajectory generation
Paraschos, A.; Neumann, G.; Peters, J.
2015. 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 15-17 October 2013, Atlanta, GA, USA, 477–483, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/HUMANOIDS.2013.7030017

Contextual policy search for generalizing a parameterized biped walking controller
Abdolmaleki, A.; Lau, N.; Reis, L. P.; Peters, J.; Neumann, G.
2015. 2015 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 8-10 April 2015, Vila Real, Portugal, 17–22, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICARSC.2015.43

Learning of non-parametric control policies with high-dimensional state features
Hoof, H. V.; Peters, J.; Neumann, G.
2015. Journal of machine learning research, 38, 995–1003

Learning multiple collaborative tasks with a mixture of interaction primitives
Ewerton, M.; Neumann, G.; Lioutikov, R.; Amor, H. B.; Peters, J.; Maeda, G.
2015. 2015 IEEE International Conference on Robotics and Automation (ICRA), 26-30 May 2015, Seattle, WA, USA, 1535–1542, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA.2015.7139393

Extracting low-dimensional control variables for movement primitives
Rueckert, E.; Mundo, J.; Paraschos, A.; Peters, J.; Neumann, G.
2015. 2015 IEEE International Conference on Robotics and Automation (ICRA), 26-30 May 2015, Seattle, WA, USA, 1511–1518, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA.2015.7139390

Towards learning hierarchical skills for multi-phase manipulation tasks
Kroemer, O.; Daniel, C.; Neumann, G.; Hoof, H. V.; Peters, J.
2015. 2015 IEEE International Conference on Robotics and Automation (ICRA), 26-30 May 2015, Seattle, WA, USA, 1503–1510, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA.2015.7139389

Model-free Probabilistic Movement Primitives for physical interaction
Paraschos, A.; Rueckert, E.; Peters, J.; Neumann, G.
2015. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 28 September - 2 October 2015, Hamburg, Germany, 2860–2866, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IROS.2015.7353771

Regularized covariance estimation for weighted maximum likelihood policy search methods
Abdolmaleki, A.; Lau, N.; Reis, L. P.; Neumann, G.
2015. 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), November 3-5, 2015, Seoul, South Korea, 154–159, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/HUMANOIDS.2015.7363529

Probabilistic segmentation applied to an assembly task
Lioutikov, R.; Neumann, G.; Maeda, G.; Peters, J.
2015. 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), 3-5 November 2015, Seoul, South Korea, 533–540, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/HUMANOIDS.2015.7363584

Learning robot in-hand manipulation with tactile features
Hoof, H. van; Hermans, T.; Neumann, G.; Peters, J.
2015. 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), 3-5 November 2015, Seoul, South Korea, 121–127, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/HUMANOIDS.2015.7363524

Policy search for path integral control
Gomez, V.; Kappen, H. J.; Peters, J.; Neumann, G.
2014. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part I. Ed.: T. Calders, 482–497, Springer-Verlag. doi:10.1007/978-3-662-44848-9_31

Policy evaluation with temporal differences: a survey and comparison
Dann, C.; Neumann, G.; Peters, J.
2014. Journal of machine learning research, 15, 809–883

Interaction primitives for human-robot cooperation tasks
Amor, H. B.; Neumann, G.; Kamthe, S.; Kroemer, O.; Peters, J.
2014. 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), 31 May - 7 June 2014, Hong Kong, China, 2831–2837, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA.2014.6907265

Learning modular policies for robotics
Neumann, G.; Daniel, C.; Paraschos, A.; Kupcsik, A.; Peters, J.
2014. Frontiers in computational neuroscience, 8, Art.-Nr.: 62. doi:10.3389/fncom.2014.00062 

Latent space policy search for robotics
Luck, K. S.; Neumann, G.; Berger, E.; Peters, J.; Amor, H. B.
2014. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 14-18 September 2014, Chicago, IL, USA, 1434–1440, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IROS.2014.6942745

Generalizing movements with information-theoretic stochastic optimal control
Lioutikov, R.; Paraschos, A.; Peters, J.; Neumann, G.
2014. Journal of aerospace information systems, 11 (9), 579–595. doi:10.2514/1.I010195

Sample-based information-theoretic stochastic optimal control
Lioutikov, R.; Paraschos, A.; Peters, J.; Neumann, G.
2014. 2014 IEEE International Conference on Robotics and Automation (ICRA), 31 May - 7 June 2014, Hong Kong, China, 3896–3902, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA.2014.6907424

Learning to predict phases of manipulation tasks as hidden states
Kroemer, O.; Hoof, H. van; Neumann, G.; Peters, J.
2014. 2014 IEEE International Conference on Robotics and Automation (ICRA), 31 May - 7 June 2014, Hong Kong, China, 4009–4014, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA.2014.6907441

Robust policy updates for stochastic optimal control
Rueckert, E.; Mindt, M.; Peters, J.; Neumann, G.
2014. 2014 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 18-20 November 2014, Madrid, Spain, 388–393, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/HUMANOIDS.2014.7041389

Learning interaction for collaborative tasks with probabilistic movement primitives
Maeda, G.; Ewerton, M.; Lioutikov, R.; Amor, H. B.; Peters, J.; Neumann, G.
2014. 2014 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 18-20 November 2014, Madrid, Spain, 527–534, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/HUMANOIDS.2014.7041413

Dimensionality reduction for probabilistic movement primitives
Colome, A.; Neumann, G.; Peters, J.; Torras, C.
2014. 2014 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 18-20 November 2014, Madrid, Spain, 794–800, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/HUMANOIDS.2014.7041454

Learned graphical models for probabilistic planning provide a new class of movement primitives
Rueckert, E. A.; Neumann, G.; Toussaint, M.; Maass, W.
2013. Frontiers in computational neuroscience, 6, Art.-Nr.: 97. doi:10.3389/fncom.2012.00097

Learning sequential motor tasks
Daniel, C.; Neumann, G.; Kroemer, O.; Peters, J.
2013. 2013 IEEE International Conference on Robotics and Automation, ICRA 2013, 6-10 May 2013, Karlsruhe, Germany, 2626–2632, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA.2013.6630937

Data-efficient generalization of robot skills with contextual policy search
Kupcsik, A. G.; Deisenroth, M. P.; Peters, J.; Neumann, G.
2013. Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013, 14-18 July 2013, Bellevue, WA, USA, 1401–1407, AAAI Press

A survey on policy search for robotics
Deisenroth, M. P.; Neumann, G.; Peters, J.
2013. Foundations and Trends in Robotics, 2 (1-2), 388–403. doi:10.1561/2300000021

Probabilistic movement primitives
Paraschos, A.; Daniel, C.; Peters, J.; Neumann, G.
2013. 27th Annual Conference on Neural Information Processing Systems, NIPS 2013, 5-10 December 2013, Lake Tahoe, NV, USA

Learning concurrent motor skills in versatile solution spaces
Daniel, C.; Neumann, G.; Peters, J.
2012. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012, 7-12 October 2012, Vilamoura, Portugal, 3591–3597, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IROS.2012.6386047

Generalization of human grasping for multi-fingered robot hands
Amor, H. B.; Kroemer, O.; Hillenbrand, U.; Neumann, G.; Peters, J.
2012. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012, 7-12 October 2012, Vilamoura, Portugal, 2043–2050, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IROS.2012.6386072

Biologically inspired kinematic synergies enable linear balance control of a humanoid robot
Hauser, H.; Neumann, G.; Ijspeert, A. J.; Maass, W.
2011. Biological cybernetics, 104 (4-5), 235–249. doi:10.1007/s00422-011-0430-1

Variational inference for policy search in changing situations
Neumann, G.
2011. Proceedings of the 28th International Conference on Machine Learning, ICML 2011, 28 June - 2 July 2011, Bellevue, WA, USA, 817–824

Fitted Q-iteration by advantage weighted regression
Neumann, G.; Peters, J.
2009. 22nd Annual Conference on Neural Information Processing Systems, NIPS 2008, 8-11 December 2008, Vancouver, BC, Canada, 1177–1184

Learning complex motions by sequencing simpler motion templates
Neumann, G.; Maass, W.; Peters, J.
2009. Proceedings of the 26th International Conference On Machine Learning, ICML 2009, 14-18 June 2009, Montreal, QC, Canada, 753–760