Past News: Before we moved our news to twitter...

2019

  • New conference papers:
    •     Bib
      Lutter, M.; Peters, J. (2019). Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems, International Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Celik, O.; Abdulsamad, H.; Peters, J. (2019). Chance-Constrained Trajectory Optimization for Nonlinear Systems with Unknown Stochastic Dynamics, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Nass, D.; Belousov, B.; Peters, J. (2019). Entropic Risk Measure in Policy Search, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Koert, D.; Pajarinen, J.; Schotschneider, A.; Trick, S., Rothkopf, C.; Peters, J. (2019). Learning Intention Aware Online Adaptation of Movement Primitives, IEEE Robotics and Automation Letters (RA-L), with presentation at the IEEE International Conference on Intelligent Robots and Systems (IROS).
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      Trick, S.; Koert, D.; Peters, J.; Rothkopf, C. (2019). Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Stark, S.; Peters, J.; Rueckert, E. (2019). Experience Reuse with Probabilistic Movement Primitives, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Liu, Z.; Hitzmann, A.; Ikemoto, S.; Stark, S.; Peters, J.; Hosoda, K. (2019). Local Online Motor Babbling: Learning Motor Abundance of a Musculoskeletal Robot Arm, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Akrour, R.; Pajarinen, J.; Neumann, G.; Peters, J. (2019). Projections for Approximate Policy Iteration Algorithms, Proceedings of the International Conference on Machine Learning (ICML).
    •     Bib
      Tosatto, S.; D'Eramo, C.; Pajarinen, J.; Restelli, M.; Peters, J. (2019). Exploration Driven By an Optimistic Bellman Equation, Proceedings of the International Joint Conference on Neural Networks (IJCNN).
    •     Bib
      Lutter, M.; Ritter, C.; Peters, J. (2019). Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning, International Conference on Learning Representations (ICLR).
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      Lauri, M.; Pajarinen, J.; Peters, J. (2019). Information gathering in decentralized POMDPs by policy graph improvement, Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
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      Wibranek, B.; Belousov, B.; Sadybakasov, A.; Tessmann, O. (2019). Interactive Assemblies: Man-Machine Collaboration through Building Components for As-Built Digital Models, Computer-Aided Architectural Design Futures (CAAD Futures).
  • New blog post on quantifying the transferability of sim-to-real control policies at sim2realai.github.io.
  • Jan Peters was appointed Fellow by the IEEE.
  • Best Paper Award at the International Conference on Advances in System Testing and Validation for M.Sc. student K.D. Gondaliya, Elmar Rueckert and Jan Peters.


  • New Journal papers:
    •     Bib
      Pajarinen, J.; Thai, H.L.; Akrour, R.; Peters, J.; Neumann, G. (2019). Compatible natural gradient policy search, Machine Learning (MLJ), 108, 8, pp.1443--1466, Springer.
    •       Bib
      Belousov, B.; Peters, J. (2019). Entropic Regularization of Markov Decision Processes, Entropy, 21, 7, MDPI.
    •     Bib
      Parisi, S.; Tangkaratt, V.; Peters, J.; Khan, M. E. (2019). TD-Regularized Actor-Critic Methods, Machine Learning (MLJ), 108, 8, pp.1467-1501.
    •       Bib
      Tanneberg, D.; Peters, J.; Rueckert, E. (2019). Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks, Neural Networks, 109, pp.67-80.
    •     Bib
      Koc, O.; Peters, J. (2019). Learning to serve: an experimental study for a new learning from demonstrations framework, IEEE Robotics and Automation Letters (ICRA/RA-L), with Presentation at the IEEE International Conference on Robotics and Automation (ICRA).
    •     Bib
      Vinogradska, J.; Bischoff, B.; Koller, T.; Achterhold, J.; Peters, J. (2020). Numerical Quadrature for Probabilistic Policy Search, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 42, 1, pp.164-175.
    •     Bib
      Brandherm, F.; Peters, J.; Neumann, G.; Akrour, R. (2019). Learning Replanning Policies with Direct Policy Search, IEEE Robotics and Automation Letters (RA-L).

2018

  • New HUMANOIDS papers:
    •     Bib
      Koert, D.; Trick, S.; Ewerton, M.; Lutter, M.; Peters, J. (2018). Online Learning of an Open-Ended Skill Library for Collaborative Tasks, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
    •     Bib
      Hoelscher, J.; Koert, D.; Peters, J.; Pajarinen, J. (2018). Utilizing Human Feedback in POMDP Execution and Specification, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
  • New CoRL paper:
    •       Bib
      Muratore, F.; Treede, F.; Gienger, M.; Peters, J. (2018). Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment, Conference on Robot Learning (CoRL).
  • New IROS paper:
    •     Bib
      Akrour, R.; Veiga, F.; Peters, J.; Neumann, G. (2018). Regularizing Reinforcement Learning with State Abstraction, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
  • New Journal Papers:
    •     Bib
      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).
    •     Bib
      Ewerton, M.; Rother, D.; Weimar, J.; Kollegger, G.; Wiemeyer, J.; Peters, J.; Maeda, G. (2018). Assisting Movement Training and Execution with Visual and Haptic Feedback, Frontiers in Neurorobotics.
    •     Bib
      Koc, O.; Maeda, G.; Peters, J. (2018). Online optimal trajectory generation for robot table tennis, Robotics and Autonomous Systems (RAS).
  • New ICML papers:
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      Parmas, P.; Doya, K.; Rasmussen, C.; Peters, J. (2018). PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos, Proceedings of the International Conference on Machine Learning (ICML).
    •     Bib
      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.
  • New ICRA papers:
    •     Bib
      Gebhardt, G.H.W.; Daun, K.; Schnaubelt, M.; Neumann, G. (2018). Learning Robust Policies for Object Manipulation with Robot Swarms, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
    •     Bib
      Pinsler, R.; Akrour, R.; Osa, T.; Peters, J.; Neumann, G. (2018). Sample and Feedback Efficient Hierarchical Reinforcement Learning from Human Preferences, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
    •     Bib
      Lioutikov, R.; Maeda, G.; Veiga, F.F.; Kersting, K.; Peters, J. (2018). Inducing Probabilistic Context-Free Grammars for the Sequencing of Robot Movement Primitives, Proceedings of the International Conference on Robotics and Automation (ICRA).
    •     Bib
      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).
  • Julia Vinogradska will receive the Best Junior Scientist Award of the Stiftung Werner-von-Siemens-Ring

2017

  • Daniel Tanneberg received the Hanns-Voith-Stiftungspreis Award 2017 for his master thesis.
  • new HUMANOIDS Papers:
    •     Bib
      Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Efficient Online Adaptation with Stochastic Recurrent Neural Networks, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
    •     Bib
      Stark, S.; Peters, J.; Rueckert, E. (2017). A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
  • New CoRL Papers:
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      Maeda, G.; Ewerton, M.; Osa, T.; Busch, B.; Peters, J. (2017). Active Incremental Learning of Robot Movement Primitives, Proceedings of the Conference on Robot Learning (CoRL).
    •     Bib
      Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals, Proceedings of the Conference on Robot Learning (CoRL).
  • New Journal Papers:
    •     Bib
      Kroemer, O.; Leischnig, S.; Luettgen, S.; Peters, J. (2018). A Kernel-based Approach to Learning Contact Distributions for Robot Manipulation Tasks, Autonomous Robots (AURO), 42, 3, pp.581-600.
    •     Bib
      Paraschos, A.; Daniel, C.; Peters, J.; Neumann, G. (2018). Using Probabilistic Movement Primitives in Robotics, Autonomous Robots (AURO), 42, 3, pp.529-551.
    •     Bib
      Lioutikov, R.; Neumann, G.; Maeda, G.; Peters, J. (2017). Learning Movement Primitive Libraries through Probabilistic Segmentation, International Journal of Robotics Research (IJRR), 36, 8, pp.879-894.
    •     Bib
      Maeda, G.; Ewerton, M.; Neumann, G.; Lioutikov, R.; Peters, J. (2017). Phase Estimation for Fast Action Recognition and Trajectory Generation in Human-Robot Collaboration, International Journal of Robotics Research (IJRR), 36, 13-14, pp.1579-1594.
    •     Bib
      van Hoof, H.; Neumann, G.; Peters, J. (2017). Non-parametric Policy Search with Limited Information Loss, Journal of Machine Learning Research (JMLR), 18, 73, pp.1-46.
    •     Bib
      van Hoof, H.; Tanneberg, D.; Peters, J. (2017). Generalized Exploration in Policy Search, Machine Learning (MLJ), 106, 9-10, pp.1705-1724.
  • New IROS papers accepted:
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      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).
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      Parisi, S.; Ramstedt, S.; Peters, J. (2017). Goal-Driven Dimensionality Reduction for Reinforcement Learning, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
    •   Bib
      Busch, B.; Maeda, G.; Mollard, Y.; Demangeat, M.; Lopes, M. (2017). Postural Optimization for an Ergonomic Human-Robot Interaction, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      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).
  • New ICML paper accepted:
    •     Bib
      Akrour, R.; Sorokin, D.; Peters, J.; Neumann, G. (2017). Local Bayesian Optimization of Motor Skills, Proceedings of the International Conference on Machine Learning (ICML).
  • JMLR review paper accepted!
    •     Bib
      Wirth, C.; Akrour, R.; Fürnkranz, J.; Neumann G. (2017). A Survey of Preference-Based Reinforcement Learning Methods, Journal of Machine Learning Research (JMLR).
  • New IJCAI paper:
    •     Bib
      Abdolmaleki, A.; Price, B.; Lau, N.; Reis, P.; Neumann, G. (2017). Contextual CMA-ES, International Joint Conference on Artificial Intelligence (IJCAI).
  • Our new GECCO paper:
    •     Bib
      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).
    is best paper finalist for the evolutionary numerical optimization track. Lets keep the fingers crossed!
  • We were on German TV.
  • Jan Peters is going to be program co-chair of the IEEE RAS Conference in Humanoid Robotics (HUMANOIDS) 2017.
  • New ICAPS 2017 paper accepted:
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      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).
  • Gerhard Neumann and Jan Peters will give a tutorial about Policy Search Methods in Robotics at AAMAS 2017.
  • Jan Peters will be Area Chair at the International Conference in Machine Learning (ICML 2017).
  • New ICRA 2017 papers accepted:
    •     Bib
      Farraj, F. B.; Osa, T.; Pedemonte, N.; Peters, J.; Neumann, G.; Giordano, P.R. (2017). A Learning-based Shared Control Architecture for Interactive Task Execution, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
    •     Bib
      Wilbers, D.; Lioutikov, R.; Peters, J. (2017). Context-Driven Movement Primitive Adaptation, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
    •     Bib
      End, F.; Akrour, R.; Peters, J.; Neumann, G. (2017). Layered Direct Policy Search for Learning Hierarchical Skills, Proceedings of the International Conference on Robotics and Automation (ICRA).
    •     Bib
      Gabriel, A.; Akrour, R.; Peters, J.; Neumann, G. (2017). Empowered Skills, Proceedings of the International Conference on Robotics and Automation (ICRA).
  • New IJRR 2017 paper:
    •     Bib
      Maeda, G.; Ewerton, M.; Neumann, G.; Lioutikov, R.; Peters, J. (2017). Phase Estimation for Fast Action Recognition and Trajectory Generation in Human-Robot Collaboration, International Journal of Robotics Research (IJRR), 36, 13-14, pp.1579-1594.
  • New AAAI-2017 papers:
    •     Bib
      Tangkaratt, V.; van Hoof, H.; Parisi, S.; Neumann, G.; Peters, J.; Sugiyama, M. (2017). Policy Search with High-Dimensional Context Variables, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
  •     Bib
    Gebhardt, G.H.W.; Kupcsik, A.; Neumann, G. (2019). The Kernel Kalman Rule, Machine Learning Journal (MLJ), 108, 12, pp.2113–2157, Springer US.
  •     Bib
    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).
  • Our undergraduate student Karl-Heinz Fiebig won the IEEE Brain Initiative Best Paper Award for the paper
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      Fiebig, K.-H. (2017). Multi-Task Logistic Regression in Brain-Computer Interfaces, Bachelor Thesis.
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      Fiebig, K.-H.; Jayaram, V.; Peters, J.; Grosse-Wentrup, M. (2016). Multi-Task Logistic Regression in Brain-Computer Interfaces, IEEE SMC 2016 — 6th Workshop on Brain-Machine Interface Systems.
    . Congratulations!
  • Jan Peters was invited for a talk at the 3rd Multidisciplinary Conference on ReinforcementLearning and Decision Making (RLDM2017).

2016

    •     Bib
      Vinogradska, J.; Bischoff, B.; Nguyen-Tuong, D.; Peters, J. (2017). Stability of Controllers for Gaussian Process Forward Models, Journal of Machine Learning Research (JMLR), 18, 100, pp.1-37.
    •     Bib
      Vinogradska, J.; Bischoff, B.; Nguyen-Tuong, D.; Romer, A.; Schmidt, H.; Peters, J. (2016). Stability of Controllers for Gaussian Process Forward Models, Proceedings of the International Conference on Machine Learning (ICML).
    •     Bib
      Akrour, R.; Abdolmaleki, A.; Abdulsamad, H.; Neumann, G. (2016). Model-Free Trajectory Optimization for Reinforcement Learning, Proceedings of the International Conference on Machine Learning (ICML).
    •   Bib
      Buechler, D.; Ott, H.; Peters, J. (2016). A Lightweight Robotic Arm with Pneumatic Muscles for Robot Learning, Proceedings of the International Conference on Robotics and Automation (ICRA).
    •     Bib
      Ewerton, M.; Maeda, G.; Neumann, G.; Kisner, V.; Kollegger, G.; Wiemeyer, J.; Peters, J. (2016). Movement Primitives with Multiple Phase Parameters, Proceedings of the International Conference on Robotics and Automation (ICRA), pp.201--206.
    •     Bib
      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).
  • Jan Peters will be Area Chair at the Robotics: Science and Systems (R:SS 2016).
  • New ADPRL 2016 paper:
    •     Bib
      Parisi, S; Blank, A; Viernickel T; Peters, J (2016). Local-utopia Policy Selection for Multi-objective Reinforcement Learning, Proceedings of the IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL).
  • New NIPS 2016 paper:
    •     Bib
      Belousov, B.; Neumann, G.; Rothkopf, C.A.; Peters, J. (2017). Catching Heuristics Are Optimal Control Policies, Proceedings of the Karniel Thirteenth Computational Motor Control Workshop.
    •     Bib
      Belousov, B.; Neumann, G.; Rothkopf, C.; Peters, J. (2016). Catching Heuristics Are Optimal Control Policies, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
  • Christian Daniel received the Best Student Paper Award at ECMLPKDD 2016 for his paper
    •     Bib
      Daniel, C.; van Hoof, H.; Peters, J.; Neumann, G. (2016). Probabilistic Inference for Determining Options in Reinforcement Learning, Machine Learning (MLJ), 104, 2-3, pp.337-357.
    in the MLJ track.
  • Five new HUMANOIDS papers:
  •     Bib
    Ewerton, M.; Maeda, G.J.; Kollegger, G.; Wiemeyer, J.; Peters, J. (2016). Incremental Imitation Learning of Context-Dependent Motor Skills, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), pp.351--358.
  •   Bib
    Gomez-Gonzalez, S.; Neumann, G.; Schoelkopf, B.; Peters, J. (2016). Using Probabilistic Movement Primitives for Striking Movements, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
  •   Bib
    Huang, Y.; Buechler, D.; Koc, O.; Schoelkopf, B.; Peters, J. (2016). Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
  •     Bib
    Koert, D.; Maeda, G.J.; Lioutikov, R.; Neumann, G.; Peters, J. (2016). Demonstration Based Trajectory Optimization for Generalizable Robot Motions, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
  •     Bib
    Tanneberg, D.; Paraschos, A.; Peters, J.; Rueckert, E. (2016). Deep Spiking Networks for Model-based Planning in Humanoids, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
    •     Bib
      Arenz, O.; Abdulsamad, H.; Neumann, G. (2016). Optimal Control and Inverse Optimal Control by Distribution Matching, Proceedings of the International Conference on Intelligent Robots and Systems (IROS), IEEE.
    •   Bib
      Koc, O.; Peters, J.; Maeda, G. (2016). A New Trajectory Generation Framework in Robotic Table Tennis, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Manschitz, S.; Gienger, M.; Kober, J.; Peters, J. (2016). Probabilistic Decomposition of Sequential Force Interaction Tasks into Movement Primitives, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      van Hoof, H.; Chen, N.; Karl, M.; van der Smagt, P.; Peters, J. (2016). Stable Reinforcement Learning with Autoencoders for Tactile and Visual Data, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Yi, Z.; Calandra, R.; Veiga, F.; van Hoof, H.; Hermans, T.; Zhang, Y.; Peters, J. (2016). Active Tactile Object Exploration with Gaussian Processes, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
  • New journal papers:
    •     Bib
      Parisi, S.; Pirotta, M.; Peters, J. (2017). Manifold-based Multi-objective Policy Search with Sample Reuse, Neurocomputing, 263, pp.3-14.
    •       Bib
      Rueckert, E.; Camernik, J.; Peters, J.; Babic, J. (2016). Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control, Nature PG: Scientific Reports, 6, 28455.
    •       Bib
      Rueckert, E.; Kappel, D.; Tanneberg, D.; Pecevski, D; Peters, J. (2016). Recurrent Spiking Networks Solve Planning Tasks, Nature PG: Scientific Reports, 6, 21142, Nature Publishing Group.
    •     Bib
      Maeda, G.; Neumann, G.; Ewerton, M.; Lioutikov, R.; Kroemer, O.; Peters, J. (2017). Probabilistic Movement Primitives for Coordination of Multiple Human-Robot Collaborative Tasks, Autonomous Robots (AURO), 41, 3, pp.593-612.
    •     Bib
      Maeda, G.; Ewerton, M.; Koert, D; Peters, J. (2016). Acquiring and Generalizing the Embodiment Mapping from Human Observations to Robot Skills, IEEE Robotics and Automation Letters (RA-L), 1, 2, pp.784--791.
    •     Bib
      Daniel, C.; Neumann, G.; Kroemer, O.; Peters, J. (2016). Hierarchical Relative Entropy Policy Search, Journal of Machine Learning Research (JMLR), 17, pp.1-50.

2015

    •     Bib
      Daniel, C.; Kroemer, O.; Viering, M.; Metz, J.; Peters, J. (2015). Active Reward Learning with a Novel Acquisition Function, Autonomous Robots (AURO), 39, pp.389-405.
    •     Bib
      Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2015). Learning Movement Primitive Attractor Goals and Sequential Skills from Kinesthetic Demonstrations, Robotics and Autonomous Systems, 74, pp.97-107.
    •     Bib
      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).
  • Jan Peters will be Area Chair at the Robotics: Science and Systems (R:SS 2015).
  • Jan Peters will be Editor and Workshop-Chair at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015).
  • Four ICRA 2015 papers accepted:
    •     Bib
      Calandra, R.; Ivaldi, S.; Deisenroth, M.;Rueckert, E.; Peters, J. (2015). Learning Inverse Dynamics Models with Contacts, Proceedings of the International Conference on Robotics and Automation (ICRA).
    •     Bib
      Ewerton, M.; Neumann, G.; Lioutikov, R.; Ben Amor, H.; Peters, J.; Maeda, G. (2015). Learning Multiple Collaborative Tasks with a Mixture of Interaction Primitives, Proceedings of the International Conference on Robotics and Automation (ICRA), pp.1535--1542.
    •     Bib
      Kroemer, O.; Daniel, C.; Neumann, G; van Hoof, H.; Peters, J. (2015). Towards Learning Hierarchical Skills for Multi-Phase Manipulation Tasks, Proceedings of the International Conference on Robotics and Automation (ICRA).
    •     Bib
      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).
  • New IROS 2015 papers accepted:
    •     Bib
      Ewerton, M.; Maeda, G.J.; Peters, J.; Neumann, G. (2015). Learning Motor Skills from Partially Observed Movements Executed at Different Speeds, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), pp.456--463.
    •     Bib
      Huang, Y.; Schoelkopf, B.; Peters, J. (2015). Learning Optimal Striking Points for A Ping-Pong Playing Robot, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2015). Probabilistic Progress Prediction and Sequencing of Concurrent Movement Primitives, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Paraschos, A.; Rueckert, E.; Peters, J; Neumann, G. (2015). Model-Free Probabilistic Movement Primitives for Physical Interaction, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Parisi, S.; Abdulsamad, H.; Paraschos, A.; Daniel, C.; Peters, J. (2015). Reinforcement Learning vs Human Programming in Tetherball Robot Games, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Veiga, F.F.; van Hoof, H.; Peters, J.; Hermans, T. (2015). Stabilizing Novel Objects by Learning to Predict Tactile Slip, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Wahrburg, A.; Zeiss, S.; Matthias, B.; Peters, J.; Ding, H. (2015). Combined Pose-Wrench and State Machine Representation for Modeling Robotic Assembly Skills, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
  • Tucker Hermans is co-organizing a workshop at RSS 2015 with Patrick van der Smagt and Heni Ben Amor on Visual and Tactile Learning
  • New AAAI paper:
    •     Bib
      Wirth, C.; Fürnkranz, J.; Neumann G. (2015). Model-Free Preference-Based Reinforcement Learning, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15).
  • Herke van Hoof got a new paper accepted:
    •     Bib
      van Hoof, H.; Peters, J.; Neumann, G. (2015). Learning of Non-Parametric Control Policies with High-Dimensional State Features, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).
  • New NIPS paper:
    •     Bib
      Abdolmaleki, A.; Lioutikov, R.; Peters, J; Lau, N.; Reis, L.; Neumann, G. (2015). Model-Based Relative Entropy Stochastic Search, Advances in Neural Information Processing Systems (NIPS / NeurIPS), MIT Press.
  • Six new HUMANOIDS 2015 papers accepted. Highlighted:
    •     Bib
      Calandra, R.; Ivaldi, S.; Deisenroth, M.; Peters, J. (2015). Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
    •     Bib
      Hoelscher, J.; Peters, J.; Hermans, T. (2015). Evaluation of Interactive Object Recognition with Tactile Sensing, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
    •       Bib
      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).

2014

    •     Bib
      Brandl, S.; Kroemer, O.; Peters, J. (2014). Generalizing Pouring Actions Between Objects using Warped Parameters, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
    •     Bib
      Colome, A.; Neumann, G.; Peters, J.; Torras, C. (2014). Dimensionality Reduction for Probabilistic Movement Primitives, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
    •     Bib
      Ivaldi, S.; Peters, J.; Padois, V.; Nori, F. (2014). Tools for simulating humanoid robot dynamics: a survey based on user feedback, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
    •     Bib
      Maeda, G.J.; Ewerton, M.; Lioutikov, R.; Amor, H.B.; Peters, J.; Neumann, G. (2014). Learning Interaction for Collaborative Tasks with Probabilistic Movement Primitives, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), pp.527--534.
    •     Bib
      Rueckert, E.; Mindt, M.; Peters, J.; Neumann, G. (2014). Robust Policy Updates for Stochastic Optimal Control, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
    •     Bib
      Wang, Z.; Boularias, A.; Muelling, K.; Schoelkopf, B.; Peters, J. (2017). Anticipatory Action Selection for Human-Robot Table Tennis, Artificial Intelligence, 247, pp.399-414.
    •     Bib
      Kupcsik, A.G.; Deisenroth, M.P.; Peters, J.; Ai Poh, L.; Vadakkepat, V.; Neumann, G. (2017). Model-based Contextual Policy Search for Data-Efficient Generalization of Robot Skills, Artificial Intelligence, 247, pp.415-439.
    •   Bib
      Dann, C.; Neumann, G.; Peters, J. (2015). Policy Evaluation with Temporal Differences: A Survey and Comparison, Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling (ICAPS), pp.359-360.
    •     Bib
      Dann, C.; Neumann, G.; Peters, J. (2014). Policy Evaluation with Temporal Differences: A Survey and Comparison, Journal of Machine Learning Research (JMLR), 15, March, pp.809-883.
    •       Bib
      Meyer, T.; Peters, J.; Zander, T.O.; Schoelkopf, B.; Grosse-Wentrup, M. (2014). Predicting Motor Learning Performance from Electroencephalographic Data, Journal of Neuroengineering and Rehabilitation, 11, 1.
    •     Bib
      Muelling, K.; Boularias, A.; Schoelkopf, B.; Peters, J. (2014). Learning Strategies in Table Tennis using Inverse Reinforcement Learning, Biological Cybernetics, 108, 5, pp.603-619.
    •     Bib
      Neumann, G.; Daniel, C.; Paraschos, A.; Kupcsik, A.; Peters, J. (2014). Learning Modular Policies for Robotics, Frontiers in Computational Neuroscience.
    •     Bib
      Wierstra, D.; Schaul, T.; Glasmachers, T.; Sun, Y.; Peters, J.; Schmidhuber, J. (2014). Natural Evolution Strategies, Journal of Machine Learning Research (JMLR), 15, March, pp.949-980.
    •     Bib
      Lioutikov, R.; Paraschos, A.; Peters, J.; Neumann, G. (2014). Generalizing Movements with Information Theoretic Stochastic Optimal Control, Journal of Aerospace Information Systems, 11, 9, pp.579-595.
  • Jan Peters will be session keynote plenary speaker at the International Conference on Intelligent Robots and Systems (IROS 2014).
  • We have received the ICRA 2014 Best Cognitive Robotics Paper Award for
    •     Bib
      Deisenroth, M.P.; Englert, P.; Peters, J.; Fox, D. (2014). Multi-Task Policy Search for Robotics, Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
    and have been finalists for the same award with our paper
    •     Bib
      Kroemer, O.; van Hoof, H.; Neumann, G.; Peters, J. (2014). Learning to Predict Phases of Manipulation Tasks as Hidden States, Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
  • Jan Peters will be invited speaker at the International Conference on Intelligent Autonomous Systems (IAS 2014) and the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2014).
  • Christian Daniel got his active reward learning paper into Robotics: Science & Systems. Well done! For more information read:
    •     Bib
      Daniel, C.; Kroemer, O.; Viering, M.; Metz, J.; Peters, J. (2015). Active Reward Learning with a Novel Acquisition Function, Autonomous Robots (AURO), 39, pp.389-405.
    •     Bib
      Daniel, C.; Viering, M.; Metz, J.; Kroemer, O.; Peters, J. (2014). Active Reward Learning, Proceedings of Robotics: Science & Systems (R:SS).
  • Four IROS 2014 papers have been accepted (100% acceptance rate for our team):
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      Chebotar, Y.; Kroemer, O.; Peters, J. (2014). Learning Robot Tactile Sensing for Object Manipulation, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Kroemer, O.; Peters, J. (2014). Predicting Object Interactions from Contact Distributions, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Luck, K.S.; Neumann, G.; Berger, E.; Peters, J.; Ben Amor, H. (2014). Latent Space Policy Search for Robotics, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2014). Learning to Sequence Movement Primitives from Demonstrations, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
  • Jan Peters will be Area Chair at Advances in Neural Information Processing Systems (NIPS 2014).
  • Six ICRA 2014 papers accepted (100% acceptance rate for our team):
    •     Bib
      Kroemer, O.; van Hoof, H.; Neumann, G.; Peters, J. (2014). Learning to Predict Phases of Manipulation Tasks as Hidden States, Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
    •     Bib
      Ben Amor, H.; Neumann, G.; Kamthe, S.; Kroemer, O.; Peters, J. (2014). Interaction Primitives for Human-Robot Cooperation Tasks , Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
    •     Bib
      Calandra, R.; Seyfarth, A.; Peters, J.; Deisenroth, M.P. (2014). An Experimental Comparison of Bayesian Optimization for Bipedal Locomotion, Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
    •     Bib
      Deisenroth, M.P.; Englert, P.; Peters, J.; Fox, D. (2014). Multi-Task Policy Search for Robotics, Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
    •     Bib
      Lioutikov, R.; Paraschos, A.; Peters, J.; Neumann, G. (2014). Sample-Based Information-Theoretic Stochastic Optimal Control, Proceedings of the International Conference on Robotics and Automation (ICRA).
    •     Bib
      Bischoff, B.; Nguyen-Tuong, D.; van Hoof, H. McHutchon, A.; Rasmussen, C.E.; Knoll, A.; Peters, J.; Deisenroth, M.P. (2014). Policy Search For Learning Robot Control Using Sparse Data, Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
    .
  • Jan Peters will be Editor at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014)
  • Jan Peters will be Area Chair at the Seventeenth International Conference on Artificial Intelligence and Statistics (AIStats 2014).

2013

  • Jan Peters picked up the IEEE Robotics & Automation Society's 2013 Early Career Award at ICRA 2013 at Karlsruhe. See also the Max Planck Press Release and the TU Darmstadt News.
  • Jens Kober has won the Georges Giralt PhD Award for the best Robotics PhD thesis in Europe in 2013.
  • Christian Daniel wins the Datenlotsenpreis 2013 for the best Master's thesis in Computer Science at TU Darmstadt.
  • Jan Peters will receive the 2013 Young Investigator Award of the International Neural Network Society (INNS). See also the Max Planck Press Release.
  • Success at NIPS 2013:
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      Carvalho, J.; Koert, D.; Daniv, M.; Peters, J. (2022). Adapting Object-Centric Probabilistic Movement Primitives with Residual Reinforcement Learning, 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids).
    •     Bib
      Vorndamme, J.; Carvalho, J.; Laha, R.; Koert, D.; Figueredo, L.; Peters, J.; Haddadin, S. (2022). Integrated Bi-Manual Motion Generation and Control shaped for Probabilistic Movement Primitives, 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids).
    •     Bib
      Knaust, M.; Koert, D. (2021). Guided Robot Skill Learning: A User-Study on Learning Probabilistic Movement Primitives with Non-Experts, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
    •     Bib
      Gomez-Gonzalez, S.; Neumann, G.; Schoelkopf, B.; Peters, J. (2020). Adaptation and Robust Learning of Probabilistic Movement Primitives, IEEE Transactions on Robotics (T-Ro), 36, 2, pp.366-379.
    •     Bib
      Motokura, K.; Takahashi, M.; Ewerton, M.; Peters, J. (2020). Plucking Motions for Tea Harvesting Robots Using Probabilistic Movement Primitives, IEEE Robotics and Automation Letters (ICRA/RA-L), with Presentation at the IEEE International Conference on Robotics and Automation (ICRA), 5, 2, pp.2377-3766.
    •     Bib
      Stark, S.; Peters, J.; Rueckert, E. (2019). Experience Reuse with Probabilistic Movement Primitives, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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      Paraschos, A.; Daniel, C.; Peters, J.; Neumann, G. (2018). Using Probabilistic Movement Primitives in Robotics, Autonomous Robots (AURO), 42, 3, pp.529-551.
    •     Bib
      Paraschos, A.; Rueckert, E.; Peters, J.; Neumann, G. (2018). Probabilistic Movement Primitives under Unknown System Dynamics, Advanced Robotics (ARJ), 32, 6, pp.297-310.
    •     Bib
      Maeda, G.; Neumann, G.; Ewerton, M.; Lioutikov, R.; Kroemer, O.; Peters, J. (2017). Probabilistic Movement Primitives for Coordination of Multiple Human-Robot Collaborative Tasks, Autonomous Robots (AURO), 41, 3, pp.593-612.
    •       Bib
      Paraschos, A. (2017). Robot Skill Representation, Learning and Control with Probabilistic Movement Primitives, PhD Thesis.
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      Dermy, O.; Paraschos, A.; Ewerton, M.; Charpillet, F.; Peters, J.; Ivaldi, S (2017). Prediction of intention during interaction with iCub with Probabilistic Movement Primitives, Frontiers in Robotics and AI, 4, pp.45.
    •   Bib
      Gomez-Gonzalez, S.; Neumann, G.; Schoelkopf, B.; Peters, J. (2016). Using Probabilistic Movement Primitives for Striking Movements, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
    •     Bib
      Klink, P (2016). Model Learning for Probabilistic Movement Primitives, Bachelor Thesis.
    •     Bib
      Paraschos, A.; Rueckert, E.; Peters, J; Neumann, G. (2015). Model-Free Probabilistic Movement Primitives for Physical Interaction, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
    •     Bib
      Ewerton, M.; Neumann, G.; Lioutikov, R.; Ben Amor, H.; Peters, J.; Maeda, G. (2015). Modeling Spatio-Temporal Variability in Human-Robot Interaction with Probabilistic Movement Primitives, Workshop on Machine Learning for Social Robotics, ICRA.
    •     Bib
      Maeda, G.J.; Ewerton, M.; Lioutikov, R.; Amor, H.B.; Peters, J.; Neumann, G. (2014). Learning Interaction for Collaborative Tasks with Probabilistic Movement Primitives, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), pp.527--534.
    •     Bib
      Colome, A.; Neumann, G.; Peters, J.; Torras, C. (2014). Dimensionality Reduction for Probabilistic Movement Primitives, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
    •     Bib
      Paraschos, A.; Daniel, C.; Peters, J.; Neumann, G (2013). Probabilistic Movement Primitives, Advances in Neural Information Processing Systems (NIPS / NeurIPS), MIT Press.
  • Our review paper "A survey of policy search in robotics" got published at Frontiers and Trends in Robotics:
    •       Bib
      Deisenroth, M. P.; Neumann, G.; Peters, J. (2013). A Survey on Policy Search for Robotics, Foundations and Trends in Robotics, 21, pp.388-403.
  • Three Humanoids papers accepted! Good Job, Alexandros Paraschos, Gerhard Neumann, Herke van Hoof, Heni Ben Amor and Oliver Kroemer!
  • Jan Peters has been invited as Action Editor for the Journal of Machine Learning Research (JMLR).
  • Jens Kober has gotten his last Ph.D.-related paper
    •       Bib
      Kober, J.; Bagnell, D.; Peters, J. (2013). Reinforcement Learning in Robotics: A Survey, International Journal of Robotics Research (IJRR), 32, 11, pp.1238-1274.
    into IJRR, the highest ranked robotics journal.
  • Gerhard Neumann, George Konidaris, Freek Stulp and Jan Peters will organize the RSS 2013 Workshop: Hierarchical and Structured Learning for Robotics.
  • Jan Peters will be Area Chair at Advances in Neural Information Processing Systems (NIPS 2013).
  • Katharina Muelling was featured in Deutschlandfunk (German Public Radio, kinda like NPR).
  • Zhikun Wang got a paper into IJRR, the highest ranked robotics journal:
    •       Bib
      Wang, Z.; Muelling, K.; Deisenroth, M. P.; Ben Amor, H.; Vogt, D.; Schoelkopf, B.; Peters, J. (2013). Probabilistic Movement Modeling for Intention Inference in Human-Robot Interaction, International Journal of Robotics Research (IJRR), 32, 7, pp.841-858.
    .
  • Katharina Muelling got a paper into IJRR, the highest ranked robotics journal:
    •       Bib
      Muelling, K.; Kober, J.; Kroemer, O.; Peters, J. (2013). Learning to Select and Generalize Striking Movements in Robot Table Tennis, International Journal of Robotics Research (IJRR), 32, 3, pp.263-279.
    .
  • Katja Mombaur, Gerhard Neumann, Martin Felis and Jan Peters will organize the ICRA 2013 Workshop: Novel Methods for Learning and Optimization of Control Policies and Trajectories for Robotics.
  • Three ICRA 2013 papers accepted: (i)
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      Daniel, C.; Neumann, G.; Kroemer, O.; Peters, J. (2013). Learning Sequential Motor Tasks, Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA).
    and (ii)
    •       Bib
      Englert, P.; Paraschos, A.; Peters, J.; Deisenroth, M. P. (2013). Model-based Imitation Learning by Probabilistic Trajectory Matching, Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA).
    •     Bib
      Englert, P. (2013). Model-based Imitation Learning by Probabilistic Trajectory Matching, Master Thesis.
    and (iii)
    •       Bib
      Gopalan, N.; Deisenroth, M. P.; Peters, J. (2013). Feedback Error Learning for Rhythmic Motor Primitives, Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA).
    .
  • Andras Kupcsik has gotten a AAAI paper accepted on his work at IAS:
    •       Bib
      Kupcsik, A.G.; Deisenroth, M.P.; Peters, J.; Neumann, G. (2013). Data-Efficient Generalization of Robot Skills with Contextual Policy Search, Proceedings of the National Conference on Artificial Intelligence (AAAI) .
    .

2012

2011