Peer-Reviewed Publications

2024

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
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, 1–8. doi:10.1109/LRA.2024.3382529
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
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

2023

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
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. International Conference on Learning Representations
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

2022

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.; Heinrich Robens, J.; 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)
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)

2021

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

2020

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

2019

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

2018

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

2017

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
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

2016

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

2015

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

2014

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

2013

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

2012

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

2011

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

2009

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

Preprints

Registered and Segmented Deformable Object Reconstruction from a Single View Point Cloud
Henrich, P.; Gyenes, B.; Scheikl, P. M.; Neumann, G.; Mathis-Ullrich, F.
2023. arxiv. doi:10.48550/arXiv.2311.07357

Swarm Reinforcement Learning For Adaptive Mesh Refinement
Freymuth, N.; Dahlinger, P.; Würth, T.; Reisch, S.; Kärger, L.; Neumann, G.
2023. arxiv. doi:10.48550/arXiv.2304.00818

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. doi:10.48550/arXiv.2307.00306

Information Maximizing Curriculum: A Curriculum-Based Approach for Training Mixtures of Experts
Blessing, D.; Celik, O.; Jia, X.; Reuss, M.; Li, M. X.; Lioutikov, R.; Neumann, G.
2023. arxiv. doi:10.48550/arXiv.2303.15349

Curriculum-Based Imitation of Versatile Skills
Li, M. X.; Celik, O.; Becker, P.; Blessing, D.; Lioutikov, R.; Neumann, G.
2023. arxiv. doi:10.48550/arXiv.2304.05171

FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion
Duffhauss, F.; Vien, N. A.; Ziesche, H.; Neumann, G.
2022. doi:10.48550/arXiv.2209.11277

Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
Shaj, V.; Büchler, D.; Sonker, R.; Becker, P.; Neumann, G.
2022. doi:10.5445/IR/1000150800

MV6D: Multi-View 6D Pose Estimation on RGB-D Frames Using a Deep Point-wise Voting Network
Duffhauss, F.; Demmler, T.; Neumann, G.
2022

What Matters For Meta-Learning Vision Regression Tasks?
Gao, N.; Ziesche, H.; Ngo, A. V.; Volpp, M.; Neumann, G.
2022. doi:10.5445/IR/1000143728

Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
Shaj Kumar, V.; Büchler, D.; Sonker, R.; Becker, P.; Neumann, G.
2022. doi:10.5445/IR/1000143406

A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning
Mohtasib, A.; Neumann, G.; Cuayáhuitl, H.
2021

Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty
Ranjbar, A.; Vien, N. A.; Ziesche, H.; Boedecker, J.; Neumann, G.
2021. doi:10.5445/IR/1000137510

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. doi:10.5445/IR/1000125269

Expected Information Maximization: Using the I-Projection for Mixture Density Estimation
Becker, P.; Arenz, O.; Neumann, G.
2020

Agricultural Robotics: The Future of Robotic Agriculture
Duckett, T.; Pearson, S.; Blackmore, S.; Grieve, B.; Chen, W.-H.; Cielniak, G.; Cleaversmith, J.; Dai, J.; Davis, S.; Fox, C.; From, P.; Georgilas, I.; Gill, R.; Gould, I.; Hanheide, M.; Iida, F.; Mihalyova, L.; Nefti-Meziani, S.; Neumann, G.; Paoletti, P.; Pridmore, T.; Ross, D.; Smith, M.; Stoelen, M.; Swainson, M.; Wane, S.; Wilson, P.; Wright, I.; Yang, G.-Z.
2018. UK-RAS Network

Peer-Reviewed Publications

Preprints

Sekretariat

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