Publications

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, 1. doi:10.1109/LRA.2020.3001539

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

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 

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, 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, 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, IEEE. doi:10.1109/IROS.2018.8593399

An algorithmic perspective on imitation learning.
Osa, T.; Pajarinen, J.; Neumann, G.; Bagnell, J. A.; Abbeel, P.; Peters, J.
2018. Now, Boston. doi:10.1561/2300000053

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, 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, IEEE

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

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, Cham. 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, Berlin. 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, 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, 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, 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, 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, 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, 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, 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, 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

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, 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, 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