- Gebhardt, G.H.W.; Daun, K.; Schnaubelt, M.; Neumann, G. (submitted). Learning Policies for Object Manipulation with Robot Swarms, Submitted to Advanced Robotics (ARJ).
- Gebhardt, G.H.W.; Hüttenrauch, M.; Neumann, G. (submitted). Using M-Embeddings to Learn Control Strategies for Robot Swarms, Submitted to Swarm Intelligence.
- 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).
- Arenz, O.; Zhong, M.; Neumann G. (2020). Trust-Region Variational Inference with Gaussian Mixture Models, Journal of Machine Learning Research (JMLR).
- Gomez-Gonzalez, S.; Neumann, G.; Schölkopf, B.; Peters, J. (2020). Adaptation and Robust Learning of Probabilistic Movement Primitives, IEEE Transactions on Robotics (T-Ro), 36, 2, pp.366-379.
- Pajarinen, J.; Arenz, O.; Peters, J.; Neumann, N. (2020). Probabilistic approach to physical object disentangling, IEEE Robotics and Automation Letters (RA-L).
- Brandherm, F.; Peters, J.; Neumann, G.; Akrour, R. (2019). Learning Replanning Policies with Direct Policy Search, IEEE Robotics and Automation Letters (RA-L).
- Gebhardt, G.H.W.; Kupcsik, A.; Neumann, G. (2019). The Kernel Kalman Rule, Machine Learning Journal (MLJ), 108, 12, pp.2113–2157, Springer US.
- 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.
- 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.
- Paraschos, A.; Daniel, C.; Peters, J.; Neumann, G. (2018). Using Probabilistic Movement Primitives in Robotics, Autonomous Robots (AURO), 42, 3, pp.529-551.
- Paraschos, A.; Rueckert, E.; Peters, J.; Neumann, G. (2018). Probabilistic Movement Primitives under Unknown System Dynamics, Advanced Robotics (ARJ), 32, 6, pp.297-310.
- 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.
- Akrour, R.; Abdolmaleki, A.; Abdulsamad, H.; Peters, J.; Neumann, G. (2018). Model-Free Trajectory-based Policy Optimization with Monotonic Improvement, Journal of Machine Learning Research (JMLR).
- Osa, T.; Peters, J.; Neumann, G. (2018). Hierarchical Reinforcement Learning of Multiple Grasping Strategies with Human Instructions, Advanced Robotics, 32, 18, pp.955-968.
- 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.
- 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.
- 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.
- 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.
- 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.
- Wirth, C.; Akrour, R.; Fürnkranz, J.; Neumann G. (2017). A Survey of Preference-Based Reinforcement Learning Methods, Journal of Machine Learning Research (JMLR).
- 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).
- 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.
- Daniel, C.; Neumann, G.; Kroemer, O.; Peters, J. (2016). Hierarchical Relative Entropy Policy Search, Journal of Machine Learning Research (JMLR), 17, pp.1-50.
- 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.
- 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.
- 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.
- Neumann, G.; Daniel, C.; Paraschos, A.; Kupcsik, A.; Peters, J. (2014). Learning Modular Policies for Robotics, Frontiers in Computational Neuroscience.
- 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.
- Rueckert, E.A.; Neumann, G.; Toussaint, M.; Maass, W. (2013). Learned graphical models for probabilistic planning provide a new class of movement primitives, Frontiers in Computational Neuroscience, 6, 97.
- Deisenroth, M. P.; Neumann, G.; Peters, J. (2013). A Survey on Policy Search for Robotics, Foundations and Trends in Robotics, 21, pp.388-403.
- Rueckert, E.A.; Neumann, G. (2012). Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation, Artificial Life.
- Hauser, H.; Neumann, G.; Ijspeert A.; Maass W. (2011). Biologically Inspired Kinematic Synergies enable Linear Balance Control of a Humanoid Robot, Biological Cybernetics.
|
- Becker, P.; Arenz, O.; Neumann, G. (2020). Expected Information Maximization: Using the I-Projection for Mixture Density Estimation, International Conference on Learning Representations (ICLR).
- Akrour, R.; Pajarinen, J.; Neumann, G.; Peters, J. (2019). Projections for Approximate Policy Iteration Algorithms, Proceedings of the International Conference on Machine Learning (ICML).
- 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).
- 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).
- 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).
- 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.
- 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).
- Akrour, R.; Peters, J.; Neumann, G. (2018). Constraint-Space Projection Direct Policy Search, European Workshops on Reinforcement Learning (EWRL).
- 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).
- 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).
- 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).
- 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).
- Gabriel, A.; Akrour, R.; Peters, J.; Neumann, G. (2017). Empowered Skills, Proceedings of the International Conference on Robotics and Automation (ICRA).
- 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).
- Abdolmaleki, A.; Price, B.; Neumann, G. (2017). Deriving and Improving CMA-ES with Information Geometric Trust Regions, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
- Abdolmaleki, A.; Price, B.; Lau, N.; Reis, P.; Neumann, G. (2017). Contextual CMA-ES, International Joint Conference on Artificial Intelligence (IJCAI).
- Akrour, R.; Sorokin, D.; Peters, J.; Neumann, G. (2017). Local Bayesian Optimization of Motor Skills, Proceedings of the International Conference on Machine Learning (ICML).
- 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.
- 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.
- 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).
- 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).
- 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.
- 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).
- 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.
- 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).
- Belousov, B.; Neumann, G.; Rothkopf, C.; Peters, J. (2016). Catching Heuristics Are Optimal Control Policies, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
- 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).
- 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).
- Abdolmaleki, A; Lau, N.; Reis, L.; Neumann, G.; (2016). Non-Parametric Contextual Stochastic Search, Proceedings of the International Conference on Intelligent Robots and Systems (IROS).
- 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).
- 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).
- 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).
- 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.
- 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).
- 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).
- 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.
- 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).
- 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).
- 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).
- Abdolmaleki, A. and Lau, N. and Reis, L. and Neumann, G. (2015). Regularized Covariance Estimation for Weighted Maximum Likelihood Policy Search Methods, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
- 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.
- 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.
- Wirth, C.; Fürnkranz, J.; Neumann G. (2015). Model-Free Preference-Based Reinforcement Learning, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15).
- Gebhardt, G.H.W.; Kupcsik, A.; Neumann, G. (2015). Learning Subspace Conditional Embedding Operators, Large-Scale Kernel Learning Workshop at ICML 2015.
- 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).
- 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).
- 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).
- Gomez, V.; Kappen, B; Peters, J.; Neumann, G (2014). Policy Search for Path Integral Control, Proceedings of the European Conference on Machine Learning (ECML).
- 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.
- Colome, A.; Neumann, G.; Peters, J.; Torras, C. (2014). Dimensionality Reduction for Probabilistic Movement Primitives, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
- 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).
- 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).
- Daniel, C.; Neumann, G.; Kroemer, O.; Peters, J. (2013). Learning Sequential Motor Tasks, Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA).
- 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.
- 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) .
- Daniel, C.; Neumann, G.; Peters, J. (2013). Autonomous Reinforcement Learning with Hierarchical REPS, Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN) .
- 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.
- Paraschos, A.; Neumann, G; Peters, J. (2013). A Probabilistic Approach to Robot Trajectory Generation, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
- Paraschos, A.; Daniel, C.; Peters, J.; Neumann, G (2013). Probabilistic Movement Primitives, Advances in Neural Information Processing Systems (NIPS / NeurIPS), MIT Press.
- Daniel, C.; Neumann, G.; Peters, J. (2012). Hierarchical Relative Entropy Policy Search, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS 2012).
- Daniel, C.; Neumann, G.; Peters, J. (2012). Learning Concurrent Motor Skills in Versatile Solution Spaces, Proceedings of the International Conference on Robot Systems (IROS).
- 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).
- Neumann, G. (2011). Variational Inference for Policy Search in Changing Situations, Proceedings of the International Conference on Machine Learning (ICML 2011) .
- Rueckert, E.A.; Neumann, G. (2011). A study of Morphological Computation by using Probabilistic Inference for Motor Planning, Proceedings of the 2nd International Conference on Morphological Computation (ICMC), pp.51--53.
- 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.
- Neumann, G.; Maass, W; Peters, J. (2009). Learning Complex Motions by Sequencing Simpler Motion Templates, Proceedings of the International Conference on Machine Learning (ICML2009).
- Hauser, H.; Neumann, G.; Ijspeert, A.; Maass, W.; (2007). Biologically Inspired Kinematic Synergies Provide a New Paradigm for Balance Control of Humanoid Robots, Proceedings of the 7th IEEE RAS/RSJ Conference on Humanoids Robots (HUMANOIDS07).
- Neumann, G.; Pfeiffer, M.; Maass, W. (2007). Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs, European Conference on Machine Learning (ECML) 2007.
|
