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ORCIDJan Peters 0001
Jan R. Peters
Person information
- affiliation: TU Darmstadt, Department of Computer Science, Germany
- affiliation: Max Planck Institute for Intelligent Systems, Tübingen, Germany
- affiliation: Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- affiliation: University of Southern California Los Angeles, Computational Learning and Motion Control Lab, CA, USA
Other persons with the same name
- Jan Peters 0002 — Flemish Institute for Technological Research, Department of Environmental Quality (and 1 more)
- Jan Peters 0003 — Fraunhofer Institute for Computer Graphics Research (IGD)
- Jan Peters 0004
![[0000-0003-4895-9256]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0003-4895-9256")
— University of Hannover, Institute of Assembly Technology, Garbsen, Germany - Jan Peters 0005 — University of Cologne, Department of Psychology, Germany
- Jan Peters 0006 — Powerledger, Perth, WA, Australia (and 1 more)
- Jan Peters 0007 — University Medical-Center Hamburg-Eppendorf, Germany
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Books and Theses
- 2014
[b1]Jens Kober, Jan Peters:
Learning Motor Skills - From Algorithms to Robot Experiments. Springer Tracts in Advanced Robotics 97, Springer 2014, ISBN 978-3-319-03193-4, pp. 1-167
Journal Articles
- 2024
- [j149]Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters:
Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics. IEEE Trans. Pattern Anal. Mach. Intell. 46(4): 1950-1963 (2024) - [j148]Piotr Kicki, Puze Liu, Davide Tateo, Haitham Bou-Ammar, Krzysztof Walas, Piotr Skrzypczynski, Jan Peters:
Fast Kinodynamic Planning on the Constraint Manifold With Deep Neural Networks. IEEE Trans. Robotics 40: 277-297 (2024) - 2023
- [j147]Shangding Gu, Alap Kshirsagar, Yali Du, Guang Chen, Jan Peters, Alois Knoll:
A human-centered safe robot reinforcement learning framework with interactive behaviors. Frontiers Neurorobotics 17 (2023) - [j146]Michael Lutter, Jan Peters:
Combining physics and deep learning to learn continuous-time dynamics models. Int. J. Robotics Res. 42(3): 83-107 (2023) - [j145]Julen Urain, Anqi Li, Puze Liu, Carlo D'Eramo, Jan Peters:
Composable energy policies for reactive motion generation and reinforcement learning. Int. J. Robotics Res. 42(10): 827-858 (2023) - [j144]Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters:
A Deterministic Approximation to Neural SDEs. IEEE Trans. Pattern Anal. Mach. Intell. 45(4): 4023-4037 (2023) - [j143]Michael Lutter, Boris Belousov, Shie Mannor, Dieter Fox, Animesh Garg, Jan Peters:
Continuous-Time Fitted Value Iteration for Robust Policies. IEEE Trans. Pattern Anal. Mach. Intell. 45(5): 5534-5548 (2023) - [j142]Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters:
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison. IEEE Trans. Pattern Anal. Mach. Intell. 45(12): 15308-15327 (2023) - [j141]Filip Bjelonic, Joonho Lee, Philip Arm, Dhionis V. Sako, Davide Tateo, Jan Peters, Marco Hutter:
Learning-Based Design and Control for Quadrupedal Robots With Parallel-Elastic Actuators. IEEE Robotics Autom. Lett. 8(3): 1611-1618 (2023) - [j140]Siwei Ju, Peter van Vliet, Oleg Arenz, Jan Peters:
Digital Twin of a Driver-in-the-Loop Race Car Simulation With Contextual Reinforcement Learning. IEEE Robotics Autom. Lett. 8(7): 4107-4114 (2023) - [j139]Dieter Büchler, Roberto Calandra, Jan Peters:
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots. Robotics Auton. Syst. 159: 104230 (2023) - [j138]Andreas Look, Barbara Rakitsch, Melih Kandemir, Jan Peters:
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. Trans. Mach. Learn. Res. 2023 (2023) - [j137]Stefan Löckel, Siwei Ju, Maximilian Schaller, Peter van Vliet, Jan Peters:
An Adaptive Human Driver Model for Realistic Race Car Simulations. IEEE Trans. Syst. Man Cybern. Syst. 53(11): 6718-6730 (2023) - 2022
- [j136]Simone Parisi, Davide Tateo, Maximilian Hensel, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning. Algorithms 15(3): 81 (2022) - [j135]Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters:
PAC-Bayesian lifelong learning for multi-armed bandits. Data Min. Knowl. Discov. 36(2): 841-876 (2022) - [j134]Fabio Muratore, Fabio Ramos, Greg Turk, Wenhao Yu, Michael Gienger, Jan Peters:
Robot Learning From Randomized Simulations: A Review. Frontiers Robotics AI 9: 799893 (2022) - [j133]Bang You, Oleg Arenz, Youping Chen, Jan Peters:
Integrating contrastive learning with dynamic models for reinforcement learning from images. Neurocomputing 476: 102-114 (2022) - [j132]Vignesh Prasad, Ruth Stock-Homburg, Jan Peters:
Human-Robot Handshaking: A Review. Int. J. Soc. Robotics 14(1): 277-293 (2022) - [j131]Alexander I. Cowen-Rivers, Wenlong Lyu, Rasul Tutunov, Zhi Wang, Antoine Grosnit, Ryan-Rhys Griffiths, Alexandre Max Maraval, Jianye Hao, Jun Wang, Jan Peters, Haitham Bou-Ammar:
HEBO: An Empirical Study of Assumptions in Bayesian Optimisation. J. Artif. Intell. Res. 74: 1269-1349 (2022) - [j130]Janosch Moos, Kay Hansel, Hany Abdulsamad, Svenja Stark, Debora Clever, Jan Peters:
Robust Reinforcement Learning: A Review of Foundations and Recent Advances. Mach. Learn. Knowl. Extr. 4(1): 276-315 (2022) - [j129]Samuele Tosatto, João Carvalho, Jan Peters:
Batch Reinforcement Learning With a Nonparametric Off-Policy Policy Gradient. IEEE Trans. Pattern Anal. Mach. Intell. 44(10): 5996-6010 (2022) - [j128]Riad Akrour, Davide Tateo, Jan Peters:
Continuous Action Reinforcement Learning From a Mixture of Interpretable Experts. IEEE Trans. Pattern Anal. Mach. Intell. 44(10): 6795-6806 (2022) - [j127]Niklas Funk, Charles B. Schaff, Rishabh Madan, Takuma Yoneda, Julen Urain De Jesus, Joe Watson, Ethan K. Gordon, Felix Widmaier, Stefan Bauer, Siddhartha S. Srinivasa, Tapomayukh Bhattacharjee, Matthew R. Walter, Jan Peters:
Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation. IEEE Robotics Autom. Lett. 7(1): 478-485 (2022) - [j126]Snehal Jauhri, Jan Peters, Georgia Chalvatzaki:
Robot Learning of Mobile Manipulation With Reachability Behavior Priors. IEEE Robotics Autom. Lett. 7(3): 8399-8406 (2022) - [j125]Tuan Dam, Georgia Chalvatzaki, Jan Peters, Joni Pajarinen:
Monte-Carlo Robot Path Planning. IEEE Robotics Autom. Lett. 7(4): 11213-11220 (2022) - [j124]Julen Urain, Davide Tateo, Jan Peters:
Learning Stable Vector Fields on Lie Groups. IEEE Robotics Autom. Lett. 7(4): 12569-12576 (2022) - [j123]Yi Zheng, Filipe Veiga, Jan Peters, Veronica J. Santos:
Autonomous Learning of Page Flipping Movements via Tactile Feedback. IEEE Trans. Robotics 38(5): 2734-2749 (2022) - [j122]Dieter Büchler, Simon Guist, Roberto Calandra, Vincent Berenz, Bernhard Schölkopf, Jan Peters:
Learning to Play Table Tennis From Scratch Using Muscular Robots. IEEE Trans. Robotics 38(6): 3850-3860 (2022) - 2021
- [j121]Niyati Rawal, Dorothea Koert, Cigdem Turan, Kristian Kersting, Jan Peters, Ruth Stock-Homburg:
ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition. Frontiers Robotics AI 8: 730317 (2021) - [j120]Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters:
MushroomRL: Simplifying Reinforcement Learning Research. J. Mach. Learn. Res. 22: 131:1-131:5 (2021) - [j119]Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning. J. Mach. Learn. Res. 22: 182:1-182:52 (2021) - [j118]Carlo D'Eramo, Andrea Cini, Alessandro Nuara, Matteo Pirotta, Cesare Alippi, Jan Peters, Marcello Restelli:
Gaussian Approximation for Bias Reduction in Q-Learning. J. Mach. Learn. Res. 22: 277:1-277:51 (2021) - [j117]Riad Akrour, Asma Atamna, Jan Peters:
Convex optimization with an interpolation-based projection and its application to deep learning. Mach. Learn. 110(8): 2267-2289 (2021) - [j116]Fabio Muratore, Michael Gienger, Jan Peters:
Assessing Transferability From Simulation to Reality for Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. 43(4): 1172-1183 (2021) - [j115]Fabio Muratore, Christian Eilers, Michael Gienger, Jan Peters:
Data-Efficient Domain Randomization With Bayesian Optimization. IEEE Robotics Autom. Lett. 6(2): 911-918 (2021) - [j114]Daniel Tanneberg, Kai Ploeger, Elmar Rueckert, Jan Peters:
SKID RAW: Skill Discovery From Raw Trajectories. IEEE Robotics Autom. Lett. 6(3): 4696-4703 (2021) - [j113]Sebastian Höfer, Kostas E. Bekris, Ankur Handa, Juan Camilo Gamboa, Melissa Mozifian, Florian Golemo, Christopher G. Atkeson, Dieter Fox, Ken Goldberg, John Leonard, C. Karen Liu, Jan Peters, Shuran Song, Peter Welinder, Martha White:
Sim2Real in Robotics and Automation: Applications and Challenges. IEEE Trans Autom. Sci. Eng. 18(2): 398-400 (2021) - [j112]Samuel Bustamante, Jan Peters, Bernhard Schölkopf, Moritz Grosse-Wentrup, Vinay Jayaram:
ArmSym: A Virtual Human-Robot Interaction Laboratory for Assistive Robotics. IEEE Trans. Hum. Mach. Syst. 51(6): 568-577 (2021) - 2020
- [j111]Mikko Lauri, Joni Pajarinen, Jan Peters:
Multi-agent active information gathering in discrete and continuous-state decentralized POMDPs by policy graph improvement. Auton. Agents Multi Agent Syst. 34(2): 42 (2020) - [j110]Marco Ewerton, Oleg Arenz, Jan Peters:
Assisted teleoperation in changing environments with a mixture of virtual guides. Adv. Robotics 34(18): 1157-1170 (2020) - [j109]Dorothea Koert, Maximilian Kircher, Vildan Salikutluk, Carlo D'Eramo, Jan Peters:
Multi-Channel Interactive Reinforcement Learning for Sequential Tasks. Frontiers Robotics AI 7: 97 (2020) - [j108]Filipe Veiga, Riad Akrour, Jan Peters:
Hierarchical Tactile-Based Control Decomposition of Dexterous In-Hand Manipulation Tasks. Frontiers Robotics AI 7: 521448 (2020) - [j107]Dorothea Koert, Susanne Trick, Marco Ewerton, Michael Lutter, Jan Peters:
Incremental Learning of an Open-Ended Collaborative Skill Library. Int. J. Humanoid Robotics 17(1): 2050001:1-2050001:23 (2020) - [j106]Rudolf Lioutikov, Guilherme Maeda, Filipe Veiga, Kristian Kersting, Jan Peters:
Learning attribute grammars for movement primitive sequencing. Int. J. Robotics Res. 39(1) (2020) - [j105]Daniel Tanneberg, Elmar Rueckert, Jan Peters:
Evolutionary training and abstraction yields algorithmic generalization of neural computers. Nat. Mach. Intell. 2(12): 753-763 (2020) - [j104]Julia Vinogradska, Bastian Bischoff, Jan Achterhold, Torsten Koller, Jan Peters:
Numerical Quadrature for Probabilistic Policy Search. IEEE Trans. Pattern Anal. Mach. Intell. 42(1): 164-175 (2020) - [j103]Sebastián Gómez-González, Sergey Prokudin, Bernhard Schölkopf, Jan Peters:
Real Time Trajectory Prediction Using Deep Conditional Generative Models. IEEE Robotics Autom. Lett. 5(2): 970-976 (2020) - [j102]Stefan Löckel, Jan Peters, Peter van Vliet:
A Probabilistic Framework for Imitating Human Race Driver Behavior. IEEE Robotics Autom. Lett. 5(2): 2086-2093 (2020) - [j101]Kurena Motokura, Masaki Takahashi, Marco Ewerton, Jan Peters:
Plucking Motions for Tea Harvesting Robots Using Probabilistic Movement Primitives. IEEE Robotics Autom. Lett. 5(2): 3275-3282 (2020) - [j100]Mikko Lauri, Joni Pajarinen, Jan Peters, Simone Frintrop:
Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization. IEEE Robotics Autom. Lett. 5(4): 5323-5330 (2020) - [j99]Joni Pajarinen, Oleg Arenz, Jan Peters, Gerhard Neumann:
Probabilistic Approach to Physical Object Disentangling. IEEE Robotics Autom. Lett. 5(4): 5510-5517 (2020) - [j98]Simon Manschitz, Michael Gienger, Jens Kober, Jan Peters:
Learning Sequential Force Interaction Skills. Robotics 9(2): 45 (2020) - [j97]Filipe Veiga, Benoni B. Edin, Jan Peters:
Grip Stabilization through Independent Finger Tactile Feedback Control. Sensors 20(6): 1748 (2020) - [j96]Sebastián Gómez-González, Gerhard Neumann, Bernhard Schölkopf, Jan Peters:
Adaptation and Robust Learning of Probabilistic Movement Primitives. IEEE Trans. Robotics 36(2): 366-379 (2020) - 2019
- [j95]Boris Belousov, Jan Peters:
Entropic Regularization of Markov Decision Processes. Entropy 21(7): 674 (2019) - [j94]Tim Schürmann, Betty J. Mohler, Jan Peters, Philipp Beckerle:
How Cognitive Models of Human Body Experience Might Push Robotics. Frontiers Neurorobotics 13: 14 (2019) - [j93]Marco Ewerton, Oleg Arenz, Guilherme Maeda, Dorothea Koert, Zlatko Kolev, Masaki Takahashi, Jan Peters:
Learning Trajectory Distributions for Assisted Teleoperation and Path Planning. Frontiers Robotics AI 6: 89 (2019) - [j92]Carlos Celemin, Guilherme Maeda, Javier Ruiz-del-Solar, Jan Peters, Jens Kober:
Reinforcement learning of motor skills using Policy Search and human corrective advice. Int. J. Robotics Res. 38(14) (2019) - [j91]Joni Pajarinen, Hong Linh Thai, Riad Akrour, Jan Peters, Gerhard Neumann:
Compatible natural gradient policy search. Mach. Learn. 108(8-9): 1443-1466 (2019) - [j90]Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan:
TD-regularized actor-critic methods. Mach. Learn. 108(8-9): 1467-1501 (2019) - [j89]Daniel Tanneberg, Jan Peters, Elmar Rueckert:
Intrinsic motivation and mental replay enable efficient online adaptation in stochastic recurrent networks. Neural Networks 109: 67-80 (2019) - [j88]Okan Koc, Jan Peters:
Learning to Serve: An Experimental Study for a New Learning From Demonstrations Framework. IEEE Robotics Autom. Lett. 4(2): 1784-1791 (2019) - [j87]Florian Brandherm, Jan Peters, Gerhard Neumann, Riad Akrour:
Learning Replanning Policies With Direct Policy Search. IEEE Robotics Autom. Lett. 4(2): 2196-2203 (2019) - [j86]Dorothea Koert, Joni Pajarinen, Albert Schotschneider, Susanne Trick, Constantin A. Rothkopf, Jan Peters:
Learning Intention Aware Online Adaptation of Movement Primitives. IEEE Robotics Autom. Lett. 4(4): 3719-3726 (2019) - [j85]Sebastián Gómez-González, Yassine Nemmour, Bernhard Schölkopf, Jan Peters:
Reliable Real-Time Ball Tracking for Robot Table Tennis. Robotics 8(4): 90 (2019) - [j84]Okan Koc, Guilherme Maeda, Jan Peters:
Optimizing the Execution of Dynamic Robot Movements With Learning Control. IEEE Trans. Robotics 35(4): 909-924 (2019) - 2018
- [j83]Alexandros Paraschos, Elmar Rueckert, Jan Peters, Gerhard Neumann:
Probabilistic movement primitives under unknown system dynamics. Adv. Robotics 32(6): 297-310 (2018) - [j82]Takayuki Osa, Jan Peters, Gerhard Neumann:
Hierarchical reinforcement learning of multiple grasping strategies with human instructions. Adv. Robotics 32(18): 955-968 (2018) - [j81]Alexandros Paraschos, Christian Daniel, Jan Peters, Gerhard Neumann:
Using probabilistic movement primitives in robotics. Auton. Robots 42(3): 529-551 (2018) - [j80]Oliver Kroemer, Simon Leischnig, Stefan Luettgen, Jan Peters:
A kernel-based approach to learning contact distributions for robot manipulation tasks. Auton. Robots 42(3): 581-600 (2018) - [j79]Marco Ewerton, David Rother, Jakob Weimar, Gerrit Kollegger, Josef Wiemeyer, Jan Peters, Guilherme Maeda:
Assisting Movement Training and Execution With Visual and Haptic Feedback. Frontiers Neurorobotics 12: 24 (2018) - [j78]Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, Jan Peters:
An Algorithmic Perspective on Imitation Learning. Found. Trends Robotics 7(1-2): 1-179 (2018) - [j77]Riad Akrour, Abbas Abdolmaleki, Hany Abdulsamad, Jan Peters, Gerhard Neumann:
Model-Free Trajectory-based Policy Optimization with Monotonic Improvement. J. Mach. Learn. Res. 19: 14:1-14:25 (2018) - [j76]Adrian Sosic, Elmar Rueckert, Jan Peters, Abdelhak M. Zoubir, Heinz Koeppl:
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling. J. Mach. Learn. Res. 19: 69:1-69:45 (2018) - [j75]Simon Manschitz, Michael Gienger, Jens Kober, Jan Peters:
Mixture of Attractors: A Novel Movement Primitive Representation for Learning Motor Skills From Demonstrations. IEEE Robotics Autom. Lett. 3(2): 926-933 (2018) - [j74]Julia Vinogradska, Bastian Bischoff, Jan Peters:
Approximate Value Iteration Based on Numerical Quadrature. IEEE Robotics Autom. Lett. 3(2): 1330-1337 (2018) - [j73]Dieter Buchler, Roberto Calandra, Bernhard Schölkopf, Jan Peters:
Control of Musculoskeletal Systems Using Learned Dynamics Models. IEEE Robotics Autom. Lett. 3(4): 3161-3168 (2018) - [j72]Okan Koc, Guilherme Maeda, Jan Peters:
Online optimal trajectory generation for robot table tennis. Robotics Auton. Syst. 105: 121-137 (2018) - [j71]Filipe Veiga, Jan Peters, Tucker Hermans:
Grip Stabilization of Novel Objects Using Slip Prediction. IEEE Trans. Haptics 11(4): 531-542 (2018) - 2017
- [j70]Zhikun Wang, Abdeslam Boularias, Katharina Mülling, Bernhard Schölkopf, Jan Peters:
Anticipatory action selection for human-robot table tennis. Artif. Intell. 247: 399-414 (2017) - [j69]Andras Gabor Kupcsik, Marc Peter Deisenroth, Jan Peters, Ai Poh Loh, Prahlad Vadakkepat, Gerhard Neumann:
Model-based contextual policy search for data-efficient generalization of robot skills. Artif. Intell. 247: 415-439 (2017) - [j68]Guilherme Maeda, Gerhard Neumann, Marco Ewerton, Rudolf Lioutikov, Oliver Kroemer, Jan Peters:
Probabilistic movement primitives for coordination of multiple human-robot collaborative tasks. Auton. Robots 41(3): 593-612 (2017) - [j67]Oriane Dermy, Alexandros Paraschos, Marco Ewerton, Jan Peters, François Charpillet, Serena Ivaldi:
Prediction of Intention during Interaction with iCub with Probabilistic Movement Primitives. Frontiers Robotics AI 4: 45 (2017) - [j66]Simone Parisi, Matteo Pirotta, Jan Peters:
Manifold-based multi-objective policy search with sample reuse. Neurocomputing 263: 3-14 (2017) - [j65]Rudolf Lioutikov, Gerhard Neumann, Guilherme Maeda, Jan Peters:
Learning movement primitive libraries through probabilistic segmentation. Int. J. Robotics Res. 36(8): 879-894 (2017) - [j64]Guilherme Maeda, Marco Ewerton, Gerhard Neumann, Rudolf Lioutikov, Jan Peters:
Phase estimation for fast action recognition and trajectory generation in human-robot collaboration. Int. J. Robotics Res. 36(13-14): 1579-1594 (2017) - [j63]Serena Ivaldi, Sébastien Lefort, Jan Peters, Mohamed Chetouani, Joelle Provasi, Elisabetta Zibetti:
Towards Engagement Models that Consider Individual Factors in HRI: On the Relation of Extroversion and Negative Attitude Towards Robots to Gaze and Speech During a Human-Robot Assembly Task - Experiments with the iCub humanoid. Int. J. Soc. Robotics 9(1): 63-86 (2017) - [j62]Herke van Hoof, Gerhard Neumann, Jan Peters:
Non-parametric Policy Search with Limited Information Loss. J. Mach. Learn. Res. 18: 73:1-73:46 (2017) - [j61]Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Jan Peters:
Stability of Controllers for Gaussian Process Dynamics. J. Mach. Learn. Res. 18: 100:1-100:37 (2017) - [j60]Herke van Hoof, Daniel Tanneberg, Jan Peters:
Generalized exploration in policy search. Mach. Learn. 106(9-10): 1705-1724 (2017) - [j59]Takayuki Osa, Amir Masoud Ghalamzan Esfahani, Rustam Stolkin, Rudolf Lioutikov, Jan Peters, Gerhard Neumann:
Guiding Trajectory Optimization by Demonstrated Distributions. IEEE Robotics Autom. Lett. 2(2): 819-826 (2017) - [j58]Oliver Kroemer, Jan Peters:
A Comparison of Autoregressive Hidden Markov Models for Multimodal Manipulations With Variable Masses. IEEE Robotics Autom. Lett. 2(2): 1101-1108 (2017) - [j57]Alexandros Paraschos, Rudolf Lioutikov, Jan Peters, Gerhard Neumann:
Probabilistic Prioritization of Movement Primitives. IEEE Robotics Autom. Lett. 2(4): 2294-2301 (2017) - [j56]Vincent Padois, Serena Ivaldi, Jan Babic, Michael N. Mistry, Jan Peters, Francesco Nori:
Whole-body multi-contact motion in humans and humanoids: Advances of the CoDyCo European project. Robotics Auton. Syst. 90: 97-117 (2017) - 2016
- [j55]Roberto Calandra, André Seyfarth, Jan Peters, Marc Peter Deisenroth:
Bayesian optimization for learning gaits under uncertainty - An experimental comparison on a dynamic bipedal walker. Ann. Math. Artif. Intell. 76(1-2): 5-23 (2016) - [j54]Abbas Abdolmaleki, Nuno Lau, Luís Paulo Reis, Jan Peters, Gerhard Neumann:
Contextual Policy Search for Linear and Nonlinear Generalization of a Humanoid Walking Controller. J. Intell. Robotic Syst. 83(3-4): 393-408 (2016) - [j53]Christian Daniel, Gerhard Neumann, Oliver Kroemer, Jan Peters:
Hierarchical Relative Entropy Policy Search. J. Mach. Learn. Res. 17: 93:1-93:50 (2016) - [j52]Christian Daniel, Herke van Hoof, Jan Peters, Gerhard Neumann:
Probabilistic inference for determining options in reinforcement learning. Mach. Learn. 104(2-3): 337-357 (2016) - [j51]Guilherme Maeda, Marco Ewerton, Dorothea Koert, Jan Peters:
Acquiring and Generalizing the Embodiment Mapping From Human Observations to Robot Skills. IEEE Robotics Autom. Lett. 1(2): 784-791 (2016) - 2015
- [j50]Christian Daniel, Oliver Kroemer, Malte Viering, Jan Metz, Jan Peters:
Active reward learning with a novel acquisition function. Auton. Robots 39(3): 389-405 (2015) - [j49]Simon Manschitz, Jens Kober, Michael Gienger, Jan Peters:
Learning movement primitive attractor goals and sequential skills from kinesthetic demonstrations. Robotics Auton. Syst. 74: 97-107 (2015) - 2014
- [j48]Botond Attila Bócsi, Lehel Csató, Jan Peters:
Indirect robot model learning for tracking control. Adv. Robotics 28(9): 589-599 (2014) - [j47]Heni Ben Amor, Ashutosh Saxena, Nicolas Hudson, Jan Peters:
Special issue on autonomous grasping and manipulation. Auton. Robots 36(1-2): 1-3 (2014) - [j46]Katharina Mülling, Abdeslam Boularias, Betty J. Mohler, Bernhard Schölkopf, Jan Peters:
Learning strategies in table tennis using inverse reinforcement learning. Biol. Cybern. 108(5): 603-619 (2014) - [j45]Gerhard Neumann, Christian Daniel, Alexandros Paraschos, Andras Gabor Kupcsik, Jan Peters:
Learning modular policies for robotics. Frontiers Comput. Neurosci. 8: 62 (2014) - [j44]Rudolf Lioutikov, Alexandros Paraschos, Jan Peters, Gerhard Neumann:
Generalizing Movements with Information-Theoretic Stochastic Optimal Control. J. Aerosp. Inf. Syst. 11(9): 579-595 (2014) - [j43]Christoph Dann, Gerhard Neumann, Jan Peters:
Policy evaluation with temporal differences: a survey and comparison. J. Mach. Learn. Res. 15(1): 809-883 (2014) - [j42]Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber:
Natural evolution strategies. J. Mach. Learn. Res. 15(1): 949-980 (2014) - [j41]Herke van Hoof, Oliver Kroemer, Jan Peters:
Probabilistic Segmentation and Targeted Exploration of Objects in Cluttered Environments. IEEE Trans. Robotics 30(5): 1198-1209 (2014) - 2013
- [j40]Peter Englert, Alexandros Paraschos, Marc Peter Deisenroth, Jan Peters:
Probabilistic model-based imitation learning. Adapt. Behav. 21(5): 388-403 (2013) - [j39]Marc Peter Deisenroth, Gerhard Neumann, Jan Peters:
A Survey on Policy Search for Robotics. Found. Trends Robotics 2(1-2): 1-142 (2013) - [j38]Katharina Mülling, Jens Kober, Oliver Kroemer, Jan Peters:
Learning to select and generalize striking movements in robot table tennis. Int. J. Robotics Res. 32(3): 263-279 (2013) - [j37]Zhikun Wang, Katharina Mülling, Marc Peter Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters:
Probabilistic movement modeling for intention inference in human-robot interaction. Int. J. Robotics Res. 32(7): 841-858 (2013) - [j36]Jens Kober, J. Andrew Bagnell, Jan Peters:
Reinforcement learning in robotics: A survey. Int. J. Robotics Res. 32(11): 1238-1274 (2013) - 2012
- [j35]Jens Kober, Andreas Wilhelm, Erhan Öztop, Jan Peters:
Reinforcement learning to adjust parametrized motor primitives to new situations. Auton. Robots 33(4): 361-379 (2012) - [j34]Yang Gao, Jan Peters, Antonios Tsourdos:
Special issue on "Bio-inspired computing for autonomous vehicles". Int. J. Intell. Comput. Cybern. 5(3) (2012) - [j33]Christoph H. Lampert, Jan Peters:
Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components. J. Real Time Image Process. 7(1): 31-41 (2012) - [j32]Duy Nguyen-Tuong, Jan Peters:
Online Kernel-Based Learning for Task-Space Tracking Robot Control. IEEE Trans. Neural Networks Learn. Syst. 23(9): 1417-1425 (2012) - 2011
- [j31]Katharina Mülling, Jens Kober, Jan Peters:
A biomimetic approach to robot table tennis. Adapt. Behav. 19(5): 359-376 (2011) - [j30]Duy Nguyen-Tuong, Jan Peters:
Model learning for robot control: a survey. Cogn. Process. 12(4): 319-340 (2011) - [j29]Duy Nguyen-Tuong, Jan Peters:
Incremental online sparsification for model learning in real-time robot control. Neurocomputing 74(11): 1859-1867 (2011) - [j28]Justus H. Piater, Sébastien Jodogne, Renaud Detry, Dirk Kraft, Norbert Krüger, Oliver Kroemer, Jan Peters:
Learning visual representations for perception-action systems. Int. J. Robotics Res. 30(3): 294-307 (2011) - [j27]Jens Kober, Jan Peters:
Policy search for motor primitives in robotics. Mach. Learn. 84(1-2): 171-203 (2011) - [j26]Hirotaka Hachiya, Jan Peters, Masashi Sugiyama:
Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning. Neural Comput. 23(11): 2798-2832 (2011) - [j25]Renaud Detry, Dirk Kraft, Oliver Kroemer, Leon Bodenhagen, Jan Peters, Norbert Krüger, Justus H. Piater:
Learning grasp affordance densities. Paladyn J. Behav. Robotics 2(1): 1-17 (2011) - [j24]Oliver Kroemer, Christoph H. Lampert, Jan Peters:
Learning Dynamic Tactile Sensing With Robust Vision-Based Training. IEEE Trans. Robotics 27(3): 545-557 (2011) - 2010
- [j23]Jan Peters, Jens Kober, Stefan Schaal:
Algorithmen zum Automatischen Erlernen von Motorfähigkeiten (Policy Learning Algorithms for Motor Skills). Autom. 58(12): 688-694 (2010) - [j22]Daan Wierstra, Alexander Förster, Jan Peters, Jürgen Schmidhuber:
Recurrent policy gradients. Log. J. IGPL 18(5): 620-634 (2010) - [j21]Tetsuro Morimura, Eiji Uchibe, Junichiro Yoshimoto, Jan Peters, Kenji Doya:
Derivatives of Logarithmic Stationary Distributions for Policy Gradient Reinforcement Learning. Neural Comput. 22(2): 342-376 (2010) - [j20]Frank Sehnke, Christian Osendorfer, Thomas Rückstieß, Alex Graves, Jan Peters, Jürgen Schmidhuber:
Parameter-exploring policy gradients. Neural Networks 23(4): 551-559 (2010) - [j19]Jens Kober, Jan Peters:
Imitation and Reinforcement Learning. IEEE Robotics Autom. Mag. 17(2): 55-62 (2010) - [j18]Oliver Kroemer, Renaud Detry, Justus H. Piater, Jan Peters:
Combining active learning and reactive control for robot grasping. Robotics Auton. Syst. 58(9): 1105-1116 (2010) - [j17]Jan Peters:
Policy gradient methods. Scholarpedia 5(11): 3698 (2010) - 2009
- [j16]Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters:
Model Learning with Local Gaussian Process Regression. Adv. Robotics 23(15): 2015-2034 (2009) - [j15]Jan Peters, Andrew Y. Ng:
Guest editorial: Special issue on robot learning, Part A. Auton. Robots 27(1): 1-2 (2009) - [j14]Jan Peters, Andrew Y. Ng:
Guest editorial: Special issue on robot learning, Part B. Auton. Robots 27(2): 91-92 (2009) - [j13]Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters:
Gaussian process dynamic programming. Neurocomputing 72(7-9): 1508-1524 (2009) - [j12]Jens Kober, Jan Peters:
Policy Search for Motor Primitives. Künstliche Intell. 23(3): 38-40 (2009) - [j11]Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters:
Adaptive importance sampling for value function approximation in off-policy reinforcement learning. Neural Networks 22(10): 1399-1410 (2009) - [j10]Jan Peters, Jun Morimoto, Russ Tedrake, Nicholas Roy:
Robot learning [TC Spotlight]. IEEE Robotics Autom. Mag. 16(3): 19-20 (2009) - 2008
- [j9]Jan Peters, Michael N. Mistry, Firdaus E. Udwadia, Jun Nakanishi, Stefan Schaal:
A unifying framework for robot control with redundant DOFs. Auton. Robots 24(1): 1-12 (2008) - [j8]Florian Steinke, Matthias Hein, Jan Peters, Bernhard Schölkopf:
Manifold-valued Thin-Plate Splines with Applications in Computer Graphics. Comput. Graph. Forum 27(2): 437-448 (2008) - [j7]Jan Peters, Stefan Schaal:
Natural Actor-Critic. Neurocomputing 71(7-9): 1180-1190 (2008) - [j6]Jan Peters, Stefan Schaal:
Learning to Control in Operational Space. Int. J. Robotics Res. 27(2): 197-212 (2008) - [j5]Jun Nakanishi, Rick Cory, Michael N. Mistry, Jan Peters, Stefan Schaal:
Operational Space Control: A Theoretical and Empirical Comparison. Int. J. Robotics Res. 27(6): 737-757 (2008) - [j4]Jan Peters:
Machine Learning for motor skills in robotics. Künstliche Intell. 22(4): 41-43 (2008) - [j3]Jan Peters, Stefan Schaal:
Reinforcement learning of motor skills with policy gradients. Neural Networks 21(4): 682-697 (2008) - 2007
- [j2]Jan Peters:
Computational Intelligence: Principles, Techniques and Applications. Comput. J. 50(6): 758 (2007) - 2002
- [j1]Jan Peters, Patrick van der Smagt:
Searching a Scalable Approach to Cerebellar Based Control. Appl. Intell. 17(1): 11-33 (2002)
Conference and Workshop Papers
- 2024
- [c282]Cedric Derstroff, Mattia Cerrato, Jannis Brugger, Jan Peters, Stefan Kramer:
Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations. AAAI 2024: 11766-11774 - [c281]Théo Vincent, Alberto Maria Metelli, Boris Belousov, Jan Peters, Marcello Restelli, Carlo D'Eramo:
Parameterized Projected Bellman Operator. AAAI 2024: 15402-15410 - [c280]Yasemin Göksu, Antonio De Almeida Correia, Vignesh Prasad, Alap Kshirsagar, Dorothea Koert, Jan Peters, Georgia Chalvatzaki:
Kinematically Constrained Human-like Bimanual Robot-to-Human Handovers. HRI (Companion) 2024: 497-501 - [c279]Fabian Hahne, Vignesh Prasad, Alap Kshirsagar, Dorothea Koert, Ruth Maria Stock-Homburg, Jan Peters, Georgia Chalvatzaki:
Transition State Clustering for Interaction Segmentation and Learning. HRI (Companion) 2024: 512-516 - 2023
- [c278]Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters:
Model-Based Uncertainty in Value Functions. AISTATS 2023: 8029-8052 - [c277]Yaonan Zhu, Shukrullo Nazirjonov, Bingheng Jiang, Jacinto E. Colan Zaita, Tadayoshi Aoyama, Yasuhisa Hasegawa, Boris Belousov, Kay Hansel, Jan Peters:
Visual Tactile Sensor Based Force Estimation for Position-Force Teleoperation. CBS 2023: 49-52 - [c276]David Rother, Thomas H. Weisswange, Jan Peters:
Disentangling Interaction Using Maximum Entropy Reinforcement Learning in Multi-Agent Systems. ECAI 2023: 1994-2001 - [c275]Christoph Zelch, Jan Peters, Oskar von Stryk:
Clustering of Motion Trajectories by a Distance Measure Based on Semantic Features. Humanoids 2023: 1-8 - [c274]Firas Al-Hafez, Davide Tateo, Oleg Arenz, Guoping Zhao, Jan Peters:
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning. ICLR 2023 - [c273]Daniel Palenicek, Michael Lutter, Joao Carvalho, Jan Peters:
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning. ICLR 2023 - [c272]Christoph Zelch, Jan Peters, Oskar von Stryk:
Start State Selection for Control Policy Learning from Optimal Trajectories. ICRA 2023: 3247-3253 - [c271]Julen Urain, Niklas Funk, Jan Peters, Georgia Chalvatzaki:
SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion. ICRA 2023: 5923-5930 - [c270]Puze Liu, Kuo Zhang, Davide Tateo, Snehal Jauhri, Zhiyuan Hu, Jan Peters, Georgia Chalvatzaki:
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction. ICRA 2023: 9449-9456 - [c269]Kay Hansel, Julen Urain, Jan Peters, Georgia Chalvatzaki:
Hierarchical Policy Blending as Inference for Reactive Robot Control. ICRA 2023: 10181-10188 - [c268]João Carvalho, An T. Le, Mark Baierl, Dorothea Koert, Jan Peters:
Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models. IROS 2023: 1916-1923 - [c267]Luca Lach, Niklas Funk, Robert Haschke, Séverin Lemaignan, Helge Joachim Ritter, Jan Peters, Georgia Chalvatzaki:
Placing by Touching: An Empirical Study on the Importance of Tactile Sensing for Precise Object Placing. IROS 2023: 8964-8971 - [c266]An T. Le, Kay Hansel, Jan Peters, Georgia Chalvatzaki:
Hierarchical Policy Blending As Optimal Transport. L4DC 2023: 797-812 - [c265]An T. Le, Georgia Chalvatzaki, Armin Biess, Jan Peters:
Accelerating Motion Planning via Optimal Transport. NeurIPS 2023 - [c264]Hamish Flynn, David Reeb, Melih Kandemir, Jan R. Peters:
Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures. NeurIPS 2023 - [c263]Theo Gruner, Boris Belousov, Fabio Muratore, Daniel Palenicek, Jan R. Peters:
Pseudo-Likelihood Inference. NeurIPS 2023 - 2022
- [c262]Marius Memmel, Puze Liu, Davide Tateo, Jan Peters:
Dimensionality Reduction and Prioritized Exploration for Policy Search. AISTATS 2022: 2134-2157 - [c261]Joe Watson, Jan Peters:
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes. CoRL 2022: 67-79 - [c260]Jonathan Vorndamme, João Carvalho, Riddhiman Laha, Dorothea Koert, Luis F. C. Figueredo, Jan Peters, Sami Haddadin:
Integrated Bi-Manual Motion Generation and Control shaped for Probabilistic Movement Primitives. Humanoids 2022: 202-209 - [c259]João Carvalho, Dorothea Koert, Marek Daniv, Jan Peters:
Adapting Object-Centric Probabilistic Movement Primitives with Residual Reinforcement Learning. Humanoids 2022: 405-412 - [c258]Vignesh Prasad, Dorothea Koert, Ruth Stock-Homburg, Jan Peters, Georgia Chalvatzaki:
MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction. Humanoids 2022: 472-479 - [c257]Rustam Galljamov, Guoping Zhao, Boris Belousov, André Seyfarth, Jan Peters:
Improving Sample Efficiency of Example-Guided Deep Reinforcement Learning for Bipedal Walking. Humanoids 2022: 587-593 - [c256]Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Boosted Curriculum Reinforcement Learning. ICLR 2022 - [c255]Pascal Klink, Haoyi Yang, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Curriculum Reinforcement Learning via Constrained Optimal Transport. ICML 2022: 11341-11358 - [c254]Kai Ploeger, Jan Peters:
Controlling the Cascade: Kinematic Planning for N-ball Toss Juggling. IROS 2022: 1139-1144 - [c253]Puze Liu, Kuo Zhang, Davide Tateo, Snehal Jauhri, Jan Peters, Georgia Chalvatzaki:
Regularized Deep Signed Distance Fields for Reactive Motion Generation. IROS 2022: 6673-6680 - [c252]Julen Urain, An T. Le, Alexander Lambert, Georgia Chalvatzaki, Byron Boots, Jan Peters:
Learning Implicit Priors for Motion Optimization. IROS 2022: 7672-7679 - [c251]Tim Schneider, Boris Belousov, Georgia Chalvatzaki, Diego Romeres, Devesh K. Jha, Jan Peters:
Active Exploration for Robotic Manipulation. IROS 2022: 9355-9362 - [c250]Niklas Funk, Svenja Menzenbach, Georgia Chalvatzaki, Jan Peters:
Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery. IROS 2022: 10215-10222 - [c249]Alessandro G. Bottero, Carlos E. Luis, Julia Vinogradska, Felix Berkenkamp, Jan Peters:
Information-Theoretic Safe Exploration with Gaussian Processes. NeurIPS 2022 - [c248]Ioannis Asmanis, Panagiotis Mermigkas, Georgia Chalvatzaki, Jan Peters, Petros Maragos:
A Semantic Enhancement of Unified Geometric Representations for Improving Indoor Visual SLAM. UR 2022: 288-294 - 2021
- [c247]Florian Stuhlenmiller, Debora Clever, Stephan Rinderknecht, Michael Lutter, Jan Peters:
Trajectory Optimization of Energy Consumption and Expected Service Life of a Robotic System. AIM 2021: 842-847 - [c246]Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, Jan Peters:
Latent Derivative Bayesian Last Layer Networks. AISTATS 2021: 1198-1206 - [c245]Joe Watson, Jan Peters:
Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk. ACC 2021: 1231-1236 - [c244]Michael Lutter, Debora Clever, René Kirsten, Kim Listmann, Jan Peters:
Building Skill Learning Systems for Robotics. CASE 2021: 1878-1883 - [c243]Puze Liu, Davide Tateo, Haitham Bou-Ammar, Jan Peters:
Robot Reinforcement Learning on the Constraint Manifold. CoRL 2021: 1357-1366 - [c242]Niklas Funk, Georgia Chalvatzaki, Boris Belousov, Jan Peters:
Learn2Assemble with Structured Representations and Search for Robotic Architectural Construction. CoRL 2021: 1401-1411 - [c241]Fabio Muratore, Theo Gruner, Florian Wiese, Boris Belousov, Michael Gienger, Jan Peters:
Neural Posterior Domain Randomization. CoRL 2021: 1532-1542 - [c240]Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Convex Regularization in Monte-Carlo Tree Search. ICML 2021: 2365-2375 - [c239]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Value Iteration in Continuous Actions, States and Time. ICML 2021: 7224-7234 - [c238]Qin Li, Georgia Chalvatzaki, Jan Peters, Yong Wang:
Directed Acyclic Graph Neural Network for Human Motion Prediction. ICRA 2021: 3197-3204 - [c237]Vignesh Prasad, Ruth Stock-Homburg, Jan Peters:
Learning Human-like Hand Reaching for Human-Robot Handshaking. ICRA 2021: 3612-3618 - [c236]Michael Lutter, Johannes Silberbauer, Joe Watson, Jan Peters:
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning. ICRA 2021: 4163-4170 - [c235]Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters:
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning. ICRA 2021: 4216-4222 - [c234]Andrew S. Morgan, Daljeet Nandha, Georgia Chalvatzaki, Carlo D'Eramo, Aaron M. Dollar, Jan Peters:
Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning. ICRA 2021: 6672-6678 - [c233]Samuele Tosatto, Georgia Chalvatzaki, Jan Peters:
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills. ICRA 2021: 10815-10821 - [c232]João Carvalho, Davide Tateo, Fabio Muratore, Jan Peters:
An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients. IJCNN 2021: 1-10 - [c231]Puze Liu, Davide Tateo, Haitham Bou-Ammar, Jan Peters:
Efficient and Reactive Planning for High Speed Robot Air Hockey. IROS 2021: 586-593 - [c230]Stefan Bauer, Manuel Wüthrich, Felix Widmaier, Annika Buchholz, Sebastian Stark, Anirudh Goyal, Thomas Steinbrenner, Joel Akpo, Shruti Joshi, Vincent Berenz, Vaibhav Agrawal, Niklas Funk, Julen Urain De Jesus, Jan Peters, Joe Watson, Claire Chen, Krishnan Srinivasan, Junwu Zhang, Jeffrey Zhang, Matthew R. Walter, Rishabh Madan, Takuma Yoneda, Denis Yarats, Arthur Allshire, Ethan K. Gordon, Tapomayukh Bhattacharjee, Siddhartha S. Srinivasa, Animesh Garg, Takahiro Maeda, Harshit Sikchi, Jilong Wang, Qingfeng Yao, Shuyu Yang, Robert McCarthy, Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens, Kevin McGuinness, Noel E. O'Connor, Stephen J. Redmond, Bernhard Schölkopf:
Real Robot Challenge: A Robotics Competition in the Cloud. NeurIPS (Competition and Demos) 2021: 190-204 - [c229]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Robust Value Iteration for Continuous Control Tasks. Robotics: Science and Systems 2021 - [c228]Julen Urain, Puze Liu, Anqi Li, Carlo D'Eramo, Jan Peters:
Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning. Robotics: Science and Systems 2021 - 2020
- [c227]Samuele Tosatto, João Carvalho, Hany Abdulsamad, Jan Peters:
A Nonparametric Off-Policy Policy Gradient. AISTATS 2020: 167-177 - [c226]Kai Ploeger, Michael Lutter, Jan Peters:
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards. CoRL 2020: 642-653 - [c225]Ruth Stock-Homburg, Jan Peters, Katharina Schneider, Vignesh Prasad, Lejla Nukovic:
Evaluation of the Handshake Turing Test for anthropomorphic Robots. HRI (Companion) 2020: 456-458 - [c224]Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters:
Sharing Knowledge in Multi-Task Deep Reinforcement Learning. ICLR 2020 - [c223]Christian Eilers, Jonas Eschmann, Robin Menzenbach, Boris Belousov, Fabio Muratore, Jan Peters:
Underactuated Waypoint Trajectory Optimization for Light Painting Photography. ICRA 2020: 1505-1510 - [c222]Christoph Zelch, Jan Peters, Oskar von Stryk:
Learning Control Policies from Optimal Trajectories. ICRA 2020: 2529-2535 - [c221]Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Generalized Mean Estimation in Monte-Carlo Tree Search. IJCAI 2020: 2397-2404 - [c220]Julen Urain, Michele Ginesi, Davide Tateo, Jan Peters:
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows. IROS 2020: 5231-5237 - [c219]Nils Rottmann, Tjasa Kunavar, Jan Babic, Jan Peters, Elmar Rueckert:
Learning Hierarchical Acquisition Functions for Bayesian Optimization. IROS 2020: 5490-5496 - [c218]Melvin Laux, Oleg Arenz, Jan Peters, Joni Pajarinen:
Deep Adversarial Reinforcement Learning for Object Disentangling. IROS 2020: 5504-5510 - [c217]Anton Ziese, Mario Daniele Fiore, Jan Peters, Uwe E. Zimmermann, Jürgen Adamy:
Redundancy resolution under hard joint constraints: a generalized approach to rank updates. IROS 2020: 7447-7453 - [c216]Leon Keller, Daniel Tanneberg, Svenja Stark, Jan Peters:
Model-Based Quality-Diversity Search for Efficient Robot Learning. IROS 2020: 9675-9680 - [c215]Allan Almeida Santos, Edwin Mora, Jan Peters, Florian Steinke:
Decentralized Data-Driven Tuning of Droop Frequency Controllers. ISGT-Europe 2020: 141-145 - [c214]Abraham Imohiosen, Joe Watson, Jan Peters:
Active Inference or Control as Inference? A Unifying View. IWAI 2020: 12-19 - [c213]Hany Abdulsamad, Jan Peters:
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation. L4DC 2020: 904-914 - [c212]Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Self-Paced Deep Reinforcement Learning. NeurIPS 2020 - [c211]Vignesh Prasad, Ruth Stock-Homburg, Jan Peters:
Advances in Human-Robot Handshaking. ICSR 2020: 478-489 - [c210]Diego Agudelo-España, Sebastián Gómez-González, Stefan Bauer, Bernhard Schölkopf, Jan Peters:
Bayesian Online Prediction of Change Points. UAI 2020: 320-329 - 2019
- [c209]Mikko Lauri, Joni Pajarinen, Jan Peters:
Information Gathering in Decentralized POMDPs by Policy Graph Improvement. AAMAS 2019: 1143-1151 - [c208]Pascal Klink, Hany Abdulsamad, Boris Belousov, Jan Peters:
Self-Paced Contextual Reinforcement Learning. CoRL 2019: 513-529 - [c207]Michael Lutter, Boris Belousov, Kim Listmann, Debora Clever, Jan Peters:
HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints. CoRL 2019: 640-650 - [c206]Joe Watson, Hany Abdulsamad, Jan Peters:
Stochastic Optimal Control as Approximate Input Inference. CoRL 2019: 697-716 - [c205]Matthias Schultheis, Boris Belousov, Hany Abdulsamad, Jan Peters:
Receding Horizon Curiosity. CoRL 2019: 1278-1288 - [c204]Boris Belousov, Alymbek Sadybakasov, Bastian Wibranek, Filipe Veiga, Oliver Tessmann, Jan Peters:
Building a Library of Tactile Skills Based on FingerVision. Humanoids 2019: 717-722 - [c203]Michael Lutter, Christian Ritter, Jan Peters:
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning. ICLR (Poster) 2019 - [c202]Riad Akrour, Joni Pajarinen, Jan Peters, Gerhard Neumann:
Projections for Approximate Policy Iteration Algorithms. ICML 2019: 181-190 - [c201]Philip Becker-Ehmck, Jan Peters, Patrick van der Smagt:
Switching Linear Dynamics for Variational Bayes Filtering. ICML 2019: 553-562 - [c200]Samuele Tosatto, Carlo D'Eramo, Joni Pajarinen, Marcello Restelli, Jan Peters:
Exploration Driven by an Optimistic Bellman Equation. IJCNN 2019: 1-8 - [c199]David Nass, Boris Belousov, Jan Peters:
Entropic Risk Measure in Policy Search. IROS 2019: 1101-1106 - [c198]Svenja Stark, Jan Peters, Elmar Rueckert:
Experience Reuse with Probabilistic Movement Primitives. IROS 2019: 1210-1217 - [c197]Julen Urain, Jan Peters:
Generalized Multiple Correlation Coefficient as a Similarity Measurement between Trajectories. IROS 2019: 1363-1369 - [c196]Marco Ewerton, Guilherme Maeda, Dorothea Koert, Zlatko Kolev, Masaki Takahashi, Jan Peters:
Reinforcement Learning of Trajectory Distributions: Applications in Assisted Teleoperation and Motion Planning. IROS 2019: 4294-4300 - [c195]Zinan Liu, Arne Hitzmann, Shuhei Ikemoto, Svenja Stark, Jan Peters, Koh Hosoda:
Local Online Motor Babbling: Learning Motor Abundance of a Musculoskeletal Robot Arm*. IROS 2019: 6594-6601 - [c194]Onur Celik, Hany Abdulsamad, Jan Peters:
Chance-Constrained Trajectory Optimization for Non-linear Systems with Unknown Stochastic Dynamics. IROS 2019: 6828-6833 - [c193]Susanne Trick, Dorothea Koert, Jan Peters, Constantin A. Rothkopf:
Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction. IROS 2019: 7009-7016 - [c192]Michael Lutter, Kim Listmann, Jan Peters:
Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems. IROS 2019: 7718-7725 - [c191]Ozan Özdenizci, Timm Meyer, Felix A. Wichmann, Jan Peters, Bernhard Schölkopf, Müjdat Çetin, Moritz Grosse-Wentrup:
Neural Signatures of Motor Skill in the Resting Brain. SMC 2019: 4387-4394 - 2018
- [c190]Fabio Muratore, Felix Treede, Michael Gienger, Jan Peters:
Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment. CoRL 2018: 700-713 - [c189]Dorothea Koert, Susanne Trick, Marco Ewerton, Michael Lutter, Jan Peters:
Online Learning of an Open-Ended Skill Library for Collaborative Tasks. Humanoids 2018: 1-9 - [c188]Janine Hoelscher, Dorothea Koert, Jan Peters, Joni Pajarinen:
Utilizing Human Feedback in POMDP Execution and Specification. Humanoids 2018: 104-111 - [c187]Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya:
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos. ICML 2018: 4062-4071 - [c186]Rudolf Lioutikov, Guilherme Maeda, Filipe Veiga, Kristian Kersting, Jan Peters:
Inducing Probabilistic Context-Free Grammars for the Sequencing of Movement Primitives. ICRA 2018: 1-8 - [c185]Robert Pinsler, Riad Akrour, Takayuki Osa, Jan Peters, Gerhard Neumann:
Sample and Feedback Efficient Hierarchical Reinforcement Learning from Human Preferences. ICRA 2018: 596-601 - [c184]Dorothea Koert, Guilherme Maeda, Gerhard Neumann, Jan Peters:
Learning Coupled Forward-Inverse Models with Combined Prediction Errors. ICRA 2018: 2433-2439 - [c183]Riad Akrour, Filipe Veiga, Jan Peters, Gerhard Neumann:
Regularizing Reinforcement Learning with State Abstraction. IROS 2018: 534-539 - 2017
- [c182]Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama:
Policy Search with High-Dimensional Context Variables. AAAI 2017: 2632-2638 - [c181]Hany Abdulsamad, Oleg Arenz, Jan Peters, Gerhard Neumann:
State-Regularized Policy Search for Linearized Dynamical Systems. ICAPS 2017: 419-424 - [c180]Guilherme Maeda, Marco Ewerton, Takayuki Osa, Baptiste Busch, Jan Peters:
Active Incremental Learning of Robot Movement Primitives. CoRL 2017: 37-46 - [c179]Daniel Tanneberg, Jan Peters, Elmar Rueckert:
Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals. CoRL 2017: 167-174 - [c178]Karl-Heinz Fiebig, Vinay Jayaram, Thomas Hesse, Alexander Blank, Jan Peters, Moritz Grosse-Wentrup:
Bayesian Regression for Artifact correction in Electroencephalography. GBCIC 2017 - [c177]Lukas Großberger, Matthias R. Hohmann, Jan Peters, Moritz Grosse-Wentrup:
Investigating Music imagery as a Cognitive Paradigm for low-Cost brain-Computer Interfaces. GBCIC 2017 - [c176]Daniel Tanneberg, Jan Peters, Elmar Rueckert:
Efficient online adaptation with stochastic recurrent neural networks. Humanoids 2017: 198-204 - [c175]Svenja Stark, Jan Peters, Elmar Rueckert:
A comparison of distance measures for learning nonparametric motor skill libraries. Humanoids 2017: 624-630 - [c174]Elmar Rueckert, Moritz Nakatenus, Samuele Tosatto, Jan Peters:
Learning inverse dynamics models in O(n) time with LSTM networks. Humanoids 2017: 811-816 - [c173]Riad Akrour, Dmitry Sorokin, Jan Peters, Gerhard Neumann:
Local Bayesian Optimization of Motor Skills. ICML 2017: 41-50 - [c172]Firas Abi-Farraj, Takayuki Osa, Nicolo Pedemonte, Jan Peters, Gerhard Neumann, Paolo Robuffo Giordano:
A learning-based shared control architecture for interactive task execution. ICRA 2017: 329-335 - [c171]Daniel Wilbers, Rudolf Lioutikov, Jan Peters:
Context-driven movement primitive adaptation. ICRA 2017: 3469-3475 - [c170]Alexander Gabriel, Riad Akrour, Jan Peters, Gerhard Neumann:
Empowered skills. ICRA 2017: 6435-6441 - [c169]Felix End, Riad Akrour, Jan Peters, Gerhard Neumann:
Layered direct policy search for learning hierarchical skills. ICRA 2017: 6442-6448 - [c168]Simone Parisi, Simon Ramstedt, Jan Peters:
Goal-driven dimensionality reduction for reinforcement learning. IROS 2017: 4634-4639 - [c167]Joni Pajarinen, Ville Kyrki, Michael C. Koval, Siddhartha S. Srinivasa, Jan Peters, Gerhard Neumann:
Hybrid control trajectory optimization under uncertainty. IROS 2017: 5694-5701 - 2016
- [c166]Guilherme Maeda, Aayush Maloo, Marco Ewerton, Rudolf Lioutikov, Jan Peters:
Anticipative Interaction Primitives for Human-Robot Collaboration. AAAI Fall Symposia 2016 - [c165]Abbas Abdolmaleki, Rudolf Lioutikov, Nuno Lau, Luís Paulo Reis, Jan Peters, Gerhard Neumann:
Model-Based Relative Entropy Stochastic Search. GECCO (Companion) 2016: 153-154 - [c164]Marco Ewerton, Guilherme Maeda, Gerrit Kollegger, Josef Wiemeyer, Jan Peters:
Incremental imitation learning of context-dependent motor skills. Humanoids 2016: 351-358 - [c163]Sebastián Gómez-González, Gerhard Neumann, Bernhard Schölkopf, Jan Peters:
Using probabilistic movement primitives for striking movements. Humanoids 2016: 502-508 - [c162]Dorothea Koert, Guilherme Maeda, Rudolf Lioutikov, Gerhard Neumann, Jan Peters:
Demonstration based trajectory optimization for generalizable robot motions. Humanoids 2016: 515-522 - [c161]Yanlong Huang, Dieter Buchler, Okan Koc, Bernhard Schölkopf, Jan Peters:
Jointly learning trajectory generation and hitting point prediction in robot table tennis. Humanoids 2016: 650-655 - [c160]Daniel Tanneberg, Alexandros Paraschos, Jan Peters, Elmar Rueckert:
Deep spiking networks for model-based planning in humanoids. Humanoids 2016: 656-661 - [c159]Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Anne Romer, Henner Schmidt, Jan Peters:
Stability of Controllers for Gaussian Process Forward Models. ICML 2016: 545-554 - [c158]Marco Ewerton, Guilherme Maeda, Gerhard Neumann, Viktor Kisner, Gerrit Kollegger, Josef Wiemeyer, Jan Peters:
Movement primitives with multiple phase parameters. ICRA 2016: 201-206 - [c157]Valerio Modugno, Gerhard Neumann, Elmar Rueckert, Giuseppe Oriolo, Jan Peters, Serena Ivaldi:
Learning soft task priorities for control of redundant robots. ICRA 2016: 221-226 - [c156]Dieter Buchler, Heiko Ott, Jan Peters:
A lightweight robotic arm with pneumatic muscles for robot learning. ICRA 2016: 4086-4092 - [c155]Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth:
Manifold Gaussian Processes for regression. IJCNN 2016: 3338-3345 - [c154]Okan Koc, Guilherme Maeda, Jan Peters:
A new trajectory generation framework in robotic table tennis. IROS 2016: 3750-3756 - [c153]Simon Manschitz, Michael Gienger, Jens Kober, Jan Peters:
Probabilistic decomposition of sequential force interaction tasks into Movement Primitives. IROS 2016: 3920-3927 - [c152]Herke van Hoof, Nutan Chen, Maximilian Karl, Patrick van der Smagt, Jan Peters:
Stable reinforcement learning with autoencoders for tactile and visual data. IROS 2016: 3928-3934 - [c151]Zhengkun Yi, Roberto Calandra, Filipe Veiga, Herke van Hoof, Tucker Hermans, Yilei Zhang, Jan Peters:
Active tactile object exploration with Gaussian processes. IROS 2016: 4925-4930 - [c150]Takayuki Osa, Jan Peters, Gerhard Neumann:
Experiments with Hierarchical Reinforcement Learning of Multiple Grasping Policies. ISER 2016: 160-172 - [c149]Boris Belousov, Gerhard Neumann, Constantin A. Rothkopf, Jan Peters:
Catching heuristics are optimal control policies. NIPS 2016: 1426-1434 - [c148]Paul Weber, Elmar Rueckert, Roberto Calandra, Jan Peters, Philipp Beckerle:
A low-cost sensor glove with vibrotactile feedback and multiple finger joint and hand motion sensing for human-robot interaction. RO-MAN 2016: 99-104 - [c147]Karl-Heinz Fiebig, Vinay Jayaram, Jan Peters, Moritz Grosse-Wentrup:
Multi-task logistic regression in brain-computer interfaces. SMC 2016: 2307-2312 - [c146]Simone Parisi, Alexander Blank, Tobias Viernickel, Jan Peters:
Local-utopia policy selection for multi-objective reinforcement learning. SSCI 2016: 1-7 - 2015
- [c145]Christoph Dann, Gerhard Neumann, Jan Peters:
Policy Evaluation with Temporal Differences: A Survey and Comparison (Extended Abstract). ICAPS 2015: 359-360 - [c144]Herke van Hoof, Jan Peters, Gerhard Neumann:
Learning of Non-Parametric Control Policies with High-Dimensional State Features. AISTATS 2015 - [c143]Okan Koc, Guilherme Maeda, Gerhard Neumann, Jan Peters:
Optimizing robot striking movement primitives with Iterative Learning Control. Humanoids 2015: 80-87 - [c142]Herke van Hoof, Tucker Hermans, Gerhard Neumann, Jan Peters:
Learning robot in-hand manipulation with tactile features. Humanoids 2015: 121-127 - [c141]Janine Hoelscher, Jan Peters, Tucker Hermans:
Evaluation of tactile feature extraction for interactive object recognition. Humanoids 2015: 310-317 - [c140]Rudolf Lioutikov, Gerhard Neumann, Guilherme Maeda, Jan Peters:
Probabilistic segmentation applied to an assembly task. Humanoids 2015: 533-540 - [c139]Simon Leischnig, Stefan Luettgen, Oliver Kroemer, Jan Peters:
A comparison of contact distribution representations for learning to predict object interactions. Humanoids 2015: 616-622 - [c138]Roberto Calandra, Serena Ivaldi, Marc Peter Deisenroth, Jan Peters:
Learning torque control in presence of contacts using tactile sensing from robot skin. Humanoids 2015: 690-695 - [c137]Lars Fritsche, Felix Unverzag, Jan Peters, Roberto Calandra:
First-person tele-operation of a humanoid robot. Humanoids 2015: 997-1002 - [c136]Abbas Abdolmaleki, Nuno Lau, Luís Paulo Reis, Jan Peters, Gerhard Neumann:
Contextual Policy Search for Generalizing a Parameterized Biped Walking Controller. ICARSC 2015: 17-22 - [c135]Oliver Kroemer, Christian Daniel, Gerhard Neumann, Herke van Hoof, Jan Peters:
Towards learning hierarchical skills for multi-phase manipulation tasks. ICRA 2015: 1503-1510 - [c134]Elmar Rueckert, Jan Mundo, Alexandros Paraschos, Jan Peters, Gerhard Neumann:
Extracting low-dimensional control variables for movement primitives. ICRA 2015: 1511-1518 - [c133]Marco Ewerton, Gerhard Neumann, Rudolf Lioutikov, Heni Ben Amor, Jan Peters, Guilherme Maeda:
Learning multiple collaborative tasks with a mixture of Interaction Primitives. ICRA 2015: 1535-1542 - [c132]Roberto Calandra, Serena Ivaldi, Marc Peter Deisenroth, Elmar Rueckert, Jan Peters:
Learning inverse dynamics models with contacts. ICRA 2015: 3186-3191 - [c131]Simon Manschitz, Jens Kober, Michael Gienger, Jan Peters:
Probabilistic progress prediction and sequencing of concurrent movement primitives. IROS 2015: 449-455 - [c130]Marco Ewerton, Guilherme Maeda, Jan Peters, Gerhard Neumann:
Learning motor skills from partially observed movements executed at different speeds. IROS 2015: 456-463 - [c129]Arne Wahrburg, Stefan Zeiss, Bjoern Matthias, Jan Peters, Hao Ding:
Combined pose-wrench and state machine representation for modeling Robotic Assembly Skills. IROS 2015: 852-857 - [c128]Alexandros Paraschos, Elmar Rueckert, Jan Peters, Gerhard Neumann:
Model-free Probabilistic Movement Primitives for physical interaction. IROS 2015: 2860-2866 - [c127]Yanlong Huang, Bernhard Schölkopf, Jan Peters:
Learning optimal striking points for a ping-pong playing robot. IROS 2015: 4587-4592 - [c126]Filipe Veiga, Herke van Hoof, Jan Peters, Tucker Hermans:
Stabilizing novel objects by learning to predict tactile slip. IROS 2015: 5065-5072 - [c125]Simone Parisi, Hany Abdulsamad, Alexandros Paraschos, Christian Daniel, Jan Peters:
Reinforcement learning vs human programming in tetherball robot games. IROS 2015: 6428-6434 - [c124]Guilherme Maeda, Gerhard Neumann, Marco Ewerton, Rudolf Lioutikov, Jan Peters:
A Probabilistic Framework for Semi-autonomous Robots Based on Interaction Primitives with Phase Estimation. ISRR (2) 2015: 253-268 - [c123]Abbas Abdolmaleki, Rudolf Lioutikov, Jan Peters, Nuno Lau, Luís Paulo Reis, Gerhard Neumann:
Model-Based Relative Entropy Stochastic Search. NIPS 2015: 3537-3545 - 2014
- [c122]Elmar Rueckert, Max Mindt, Jan Peters, Gerhard Neumann:
Robust policy updates for stochastic optimal control. Humanoids 2014: 388-393 - [c121]Guilherme Maeda, Marco Ewerton, Rudolf Lioutikov, Heni Ben Amor, Jan Peters, Gerhard Neumann:
Learning interaction for collaborative tasks with probabilistic movement primitives. Humanoids 2014: 527-534 - [c120]Sascha Brandi, Oliver Kroemer, Jan Peters:
Generalizing pouring actions between objects using warped parameters. Humanoids 2014: 616-621 - [c119]Adria Colome, Gerhard Neumann, Jan Peters, Carme Torras:
Dimensionality reduction for probabilistic movement primitives. Humanoids 2014: 794-800 - [c118]Serena Ivaldi, Jan Peters, Vincent Padois, Francesco Nori:
Tools for simulating humanoid robot dynamics: A survey based on user feedback. Humanoids 2014: 842-849 - [c117]Rudolf Lioutikov, Oliver Kroemer, Guilherme Maeda, Jan Peters:
Learning Manipulation by Sequencing Motor Primitives with a Two-Armed Robot. IAS 2014: 1601-1611 - [c116]Abbas Abdolmaleki, Nima Shafii, Luís Paulo Reis, Nuno Lau, Jan Peters, Gerhard Neumann:
Omnidirectional Walking with a Compliant Inverted Pendulum Model. IBERAMIA 2014: 481-493 - [c115]Sanket Kamthe, Jan Peters, Marc Peter Deisenroth:
Multi-modal filtering for non-linear estimation. ICASSP 2014: 7979-7983 - [c114]Roberto Calandra, André Seyfarth, Jan Peters, Marc Peter Deisenroth:
An experimental comparison of Bayesian optimization for bipedal locomotion. ICRA 2014: 1951-1958 - [c113]Heni Ben Amor, Gerhard Neumann, Sanket Kamthe, Oliver Kroemer, Jan Peters:
Interaction primitives for human-robot cooperation tasks. ICRA 2014: 2831-2837 - [c112]Marc Peter Deisenroth, Peter Englert, Jan Peters, Dieter Fox:
Multi-task policy search for robotics. ICRA 2014: 3876-3881 - [c111]Bastian Bischoff, Duy Nguyen-Tuong, Herke van Hoof, Andrew McHutchon, Carl E. Rasmussen, Alois C. Knoll, Jan Peters, Marc Peter Deisenroth:
Policy search for learning robot control using sparse data. ICRA 2014: 3882-3887 - [c110]Rudolf Lioutikov, Alexandros Paraschos, Jan Peters, Gerhard Neumann:
Sample-based informationl-theoretic stochastic optimal control. ICRA 2014: 3896-3902 - [c109]Oliver Kroemer, Herke van Hoof, Gerhard Neumann, Jan Peters:
Learning to predict phases of manipulation tasks as hidden states. ICRA 2014: 4009-4014 - [c108]Kevin Sebastian Luck, Gerhard Neumann, Erik Berger, Jan Peters, Heni Ben Amor:
Latent space policy search for robotics. IROS 2014: 1434-1440 - [c107]Oliver Kroemer, Jan Peters:
Predicting object interactions from contact distributions. IROS 2014: 3361-3367 - [c106]Yevgen Chebotar, Oliver Kroemer, Jan Peters:
Learning robot tactile sensing for object manipulation. IROS 2014: 3368-3375 - [c105]Simon Manschitz, Jens Kober, Michael Gienger, Jan Peters:
Learning to sequence movement primitives from demonstrations. IROS 2014: 4414-4421 - [c104]Roberto Calandra, Nakul Gopalan, André Seyfarth, Jan Peters, Marc Peter Deisenroth:
Bayesian Gait Optimization for Bipedal Locomotion. LION 2014: 274-290 - [c103]Vicenç Gómez, Hilbert J. Kappen, Jan Peters, Gerhard Neumann:
Policy Search for Path Integral Control. ECML/PKDD (1) 2014: 482-497 - [c102]Christian Daniel, Malte Viering, Jan Metz, Oliver Kroemer, Jan Peters:
Active Reward Learning. Robotics: Science and Systems 2014 - 2013
- [c101]Andras Gabor Kupcsik, Marc Peter Deisenroth, Jan Peters, Gerhard Neumann:
Data-Efficient Generalization of Robot Skills with Contextual Policy Search. AAAI 2013: 1401-1407 - [c100]Herke van Hoof, Oliver Kroemer, Jan Peters:
Probabilistic interactive segmentation for anthropomorphic robots in cluttered environments. Humanoids 2013: 169-176 - [c99]Alexandros Paraschos, Gerhard Neumann, Jan Peters:
A probabilistic approach to robot trajectory generation. Humanoids 2013: 477-483 - [c98]Nakul Gopalan, Marc Peter Deisenroth, Jan Peters:
Feedback error learning for rhythmic motor primitives. ICRA 2013: 1317-1322 - [c97]Peter Englert, Alexandros Paraschos, Jan Peters, Marc Peter Deisenroth:
Model-based imitation learning by probabilistic trajectory matching. ICRA 2013: 1922-1927 - [c96]Christian Daniel, Gerhard Neumann, Oliver Kroemer, Jan Peters:
Learning sequential motor tasks. ICRA 2013: 2626-2632 - [c95]Botond Bocsi, Lehel Csató, Jan Peters:
Alignment-based transfer learning for robot models. IJCNN 2013: 1-7 - [c94]Christian Daniel, Gerhard Neumann, Jan Peters:
Autonomous reinforcement learning with hierarchical REPS. IJCNN 2013: 1-8 - [c93]Heni Ben Amor, David Vogt, Marco Ewerton, Erik Berger, Bernhard Jung, Jan Peters:
Learning responsive robot behavior by imitation. IROS 2013: 3257-3264 - [c92]Alexandros Paraschos, Christian Daniel, Jan Peters, Gerhard Neumann:
Probabilistic Movement Primitives. NIPS 2013: 2616-2624 - [c91]Jan Peters, Jens Kober, Katharina Mülling, Oliver Krömer, Gerhard Neumann:
Towards Robot Skill Learning: From Simple Skills to Table Tennis. ECML/PKDD (3) 2013: 627-631 - 2012
- [c90]Katharina Mülling, Jens Kober, Oliver Kroemer, Jan Peters:
Learning to Select and Generalize Striking Movements in Robot Table Tennis. AAAI Fall Symposium: Robots Learning Interactively from Human Teachers 2012 - [c89]Jan Peters, Katharina Mülling, Jens Kober, Duy Nguyen-Tuong, Oliver Krömer:
Robot Skill Learning. ECAI 2012: 40-45 - [c88]Marc Peter Deisenroth, Csaba Szepesvári, Jan Peters:
Preface. EWRL 2012 - [c87]Oliver Kroemer, Heni Ben Amor, Marco Ewerton, Jan Peters:
Point cloud completion using extrusions. Humanoids 2012: 680-685 - [c86]Botond Bocsi, Philipp Hennig, Lehel Csató, Jan Peters:
Learning tracking control with forward models. ICRA 2012: 259-264 - [c85]Oliver Kroemer, Emre Ugur, Erhan Öztop, Jan Peters:
A kernel-based approach to direct action perception. ICRA 2012: 2605-2610 - [c84]Marc Peter Deisenroth, Roberto Calandra, André Seyfarth, Jan Peters:
Toward fast policy search for learning legged locomotion. IROS 2012: 1787-1792 - [c83]Heni Ben Amor, Oliver Kroemer, Ulrich Hillenbrand, Gerhard Neumann, Jan Peters:
Generalization of human grasping for multi-fingered robot hands. IROS 2012: 2043-2050 - [c82]Christian Daniel, Gerhard Neumann, Jan Peters:
Learning concurrent motor skills in versatile solution spaces. IROS 2012: 3591-3597 - [c81]Timm Meyer, Jan Peters, Doris Brtz, Thorsten O. Zander, Bernhard Schölkopf, Surjo R. Soekadar, Moritz Grosse-Wentrup:
A brain-robot interface for studying motor learning after stroke. IROS 2012: 4078-4083 - [c80]Herke van Hoof, Oliver Kroemer, Heni Ben Amor, Jan Peters:
Maximally informative interaction learning for scene exploration. IROS 2012: 5152-5158 - [c79]Jens Kober, Katharina Mülling, Jan Peters:
Learning throwing and catching skills. IROS 2012: 5167-5168 - [c78]Abdeslam Boularias, Oliver Kroemer, Jan Peters:
Algorithms for Learning Markov Field Policies. NIPS 2012: 2186-2194 - [c77]Abdeslam Boularias, Oliver Krömer, Jan Peters:
Structured Apprenticeship Learning. ECML/PKDD (2) 2012: 227-242 - [c76]Zhikun Wang, Marc Peter Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters:
Probabilistic Modeling of Human Movements for Intention Inference. Robotics: Science and Systems 2012 - [c75]Christian Daniel, Gerhard Neumann, Jan Peters:
Hierarchical Relative Entropy Policy Search. AISTATS 2012: 273-281 - 2011
- [c74]Zhikun Wang, Abdeslam Boularias, Katharina Mülling, Jan Peters:
Balancing Safety and Exploitability in Opponent Modeling. AAAI 2011: 1515-1520 - [c73]Zhikun Wang, Abdeslam Boularias, Katharina Mülling, Jan Peters:
Modeling Opponent Actions for Table-Tennis Playing Robot. AAAI 2011: 1828-1829 - [c72]Oliver Kroemer, Jan Peters:
Active exploration for robot parameter selection in episodic reinforcement learning. ADPRL 2011: 25-31 - [c71]Oliver Kroemer, Jan Peters:
A flexible hybrid framework for modeling complex manipulation tasks. ICRA 2011: 1856-1861 - [c70]Roberto Lampariello, Duy Nguyen-Tuong, Claudio Castellini, Gerd Hirzinger, Jan Peters:
Trajectory planning for optimal robot catching in real-time. ICRA 2011: 3719-3726 - [c69]Jens Kober, Erhan Öztop, Jan Peters:
Reinforcement Learning to Adjust Robot Movements to New Situations. IJCAI 2011: 2650-2655 - [c68]Zhikun Wang, Christoph H. Lampert, Katharina Mülling, Bernhard Schölkopf, Jan Peters:
Learning anticipation policies for robot table tennis. IROS 2011: 332-337 - [c67]Jens Kober, Jan Peters:
Learning elementary movements jointly with a higher level task. IROS 2011: 338-343 - [c66]Botond Bocsi, Duy Nguyen-Tuong, Lehel Csató, Bernhard Schölkopf, Jan Peters:
Learning inverse kinematics with structured prediction. IROS 2011: 698-703 - [c65]Duy Nguyen-Tuong, Jan Peters:
Learning task-space tracking control with kernels. IROS 2011: 704-709 - [c64]Abdeslam Boularias, Oliver Kroemer, Jan Peters:
Learning robot grasping from 3-D images with Markov Random Fields. IROS 2011: 1548-1553 - [c63]Oliver Kroemer, Jan Peters:
A Non-Parametric Approach to Dynamic Programming. NIPS 2011: 1719-1727 - [c62]Abdeslam Boularias, Jens Kober, Jan Peters:
Relative Entropy Inverse Reinforcement Learning. AISTATS 2011: 182-189 - 2010
- [c61]Jan Peters, Katharina Mülling, Yasemin Altun:
Relative Entropy Policy Search. AAAI 2010: 1607-1612 - [c60]Katharina Mülling, Jens Kober, Jan Peters:
Learning table tennis with a Mixture of Motor Primitives. Humanoids 2010: 411-416 - [c59]Oliver Kroemer, Renaud Detry, Justus H. Piater, Jan Peters:
Grasping with Vision Descriptors and Motor Primitives. ICINCO (2) 2010: 47-54 - [c58]Oliver Kroemer, Renaud Detry, Justus H. Piater, Jan Peters:
Grasping with Vision Descriptors and Motor Primitives. ICINCO (Selected Papers) 2010: 211-223 - [c57]Jens Kober, Katharina Mülling, Oliver Kroemer, Christoph H. Lampert, Bernhard Schölkopf, Jan Peters:
Movement templates for learning of hitting and batting. ICRA 2010: 853-858 - [c56]Duy Nguyen-Tuong, Jan Peters:
Using model knowledge for learning inverse dynamics. ICRA 2010: 2677-2682 - [c55]Ayse Erkan, Oliver Kroemer, Renaud Detry, Yasemin Altun, Justus H. Piater, Jan Peters:
Learning probabilistic discriminative models of grasp affordances under limited supervision. IROS 2010: 1586-1591 - [c54]Katharina Mülling, Jens Kober, Jan Peters:
A biomimetic approach to robot table tennis. IROS 2010: 1921-1926 - [c53]Jan Peters, Katharina Mülling, Jens Kober:
Experiments with Motor Primitives in Table Tennis. ISER 2010: 347-359 - [c52]Mauricio A. Álvarez, Jan Peters, Bernhard Schölkopf, Neil D. Lawrence:
Switched Latent Force Models for Movement Segmentation. NIPS 2010: 55-63 - [c51]Silvia Chiappa, Jan Peters:
Movement extraction by detecting dynamics switches and repetitions. NIPS 2010: 388-396 - [c50]Jens Kober, Erhan Öztop, Jan Peters:
Reinforcement Learning to adjust Robot Movements to New Situations. Robotics: Science and Systems 2010 - [c49]Oliver Krömer, Renaud Detry, Justus H. Piater, Jan Peters:
Adapting Preshaped Grasping Movements Using Vision Descriptors. SAB 2010: 156-166 - [c48]Katharina Mülling, Jens Kober, Jan Peters:
Simulating Human Table Tennis with a Biomimetic Robot Setup. SAB 2010: 273-282 - [c47]Manuel Gomez-Rodriguez, Jan Peters, N. Jeremy Hill, Bernhard Schölkopf, Alireza Gharabaghi, Moritz Grosse-Wentrup:
Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement imagery. SMC 2010: 121-126 - [c46]Duy Nguyen-Tuong, Jan Peters:
Incremental Sparsification for Real-time Online Model Learning. AISTATS 2010: 557-564 - 2009
- [c45]Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters:
Efficient data reuse in value function approximation. ADPRL 2009: 8-15 - [c44]Jan Peters, Jens Kober:
Using reward-weighted imitation for robot Reinforcement Learning. ADPRL 2009: 226-232 - [c43]Katharina Mülling, Jan Peters:
A Computational Model of Human Table Tennis for Robot Application. AMS 2009: 57-64 - [c42]Jens Kober, Jan Peters:
Learning New Basic Movements for Robotics. AMS 2009: 105-112 - [c41]Christoph H. Lampert, Jan Peters:
Active Structured Learning for High-Speed Object Detection. DAGM-Symposium 2009: 221-231 - [c40]Gerhard Neumann, Wolfgang Maass, Jan Peters:
Learning complex motions by sequencing simpler motion templates. ICML 2009: 753-760 - [c39]Jens Kober, Jan Peters:
Learning motor primitives for robotics. ICRA 2009: 2112-2118 - [c38]Oliver Kroemer, Renaud Detry, Justus H. Piater, Jan Peters:
Active learning using mean shift optimization for robot grasping. IROS 2009: 2610-2615 - [c37]Duy Nguyen-Tuong, Bernhard Schölkopf, Jan Peters:
Sparse online model learning for robot control with support vector regression. IROS 2009: 3121-3126 - [c36]Justus H. Piater, Sébastien Jodogne, Renaud Detry, Dirk Kraft, Norbert Krüger, Oliver Krömer, Jan Peters:
Learning Visual Representations for Interactive Systems. ISRR 2009: 399-416 - [c35]Jan Peters, Katharina Mülling, Jens Kober, Duy Nguyen-Tuong, Oliver Krömer:
Towards Motor Skill Learning for Robotics. ISRR 2009: 469-482 - [c34]Hirotaka Hachiya, Jan Peters, Masashi Sugiyama:
Efficient Sample Reuse in EM-Based Policy Search. ECML/PKDD (1) 2009: 469-484 - [c33]Matthew Hoffman, Nando de Freitas, Arnaud Doucet, Jan Peters:
An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward. AISTATS 2009: 232-239 - 2008
- [c32]Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters:
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation. AAAI 2008: 1351-1356 - [c31]Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters:
Computed torque control with nonparametric regression models. ACC 2008: 212-217 - [c30]Marc Peter Deisenroth, Jan Peters, Carl E. Rasmussen:
Approximate dynamic programming with Gaussian processes. ACC 2008: 4480-4485 - [c29]Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber:
Natural Evolution Strategies. IEEE Congress on Evolutionary Computation 2008: 3381-3387 - [c28]Duy Nguyen-Tuong, Jan Peters, Matthias W. Seeger, Bernhard Schölkopf:
Learning Inverse Dynamics: a Comparison. ESANN 2008: 13-18 - [c27]Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters:
Model-Based Reinforcement Learning with Continuous States and Actions. ESANN 2008: 19-24 - [c26]Jan Peters, Jens Kober, Duy Nguyen-Tuong:
Policy Learning - A Unified Perspective with Applications in Robotics. EWRL 2008: 220-228 - [c25]Frank Sehnke, Christian Osendorfer, Thomas Rückstieß, Alex Graves, Jan Peters, Jürgen Schmidhuber:
Policy Gradients with Parameter-Based Exploration for Control. ICANN (1) 2008: 387-396 - [c24]Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber:
Episodic Reinforcement Learning by Logistic Reward-Weighted Regression. ICANN (1) 2008: 407-416 - [c23]Jan Peters, Duy Nguyen-Tuong:
Real-time learning of resolved velocity control on a Mitsubishi PA-10. ICRA 2008: 2872-2877 - [c22]Duy Nguyen-Tuong, Jan Peters:
Local Gaussian process regression for real-time model-based robot control. IROS 2008: 380-385 - [c21]Jens Kober, Betty J. Mohler, Jan Peters:
Learning perceptual coupling for motor primitives. IROS 2008: 834-839 - [c20]Silvia Chiappa, Jens Kober, Jan Peters:
Using Bayesian Dynamical Systems for Motion Template Libraries. NIPS 2008: 297-304 - [c19]Jens Kober, Jan Peters:
Policy Search for Motor Primitives in Robotics. NIPS 2008: 849-856 - [c18]Gerhard Neumann, Jan Peters:
Fitted Q-iteration by Advantage Weighted Regression. NIPS 2008: 1177-1184 - [c17]Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters:
Local Gaussian Process Regression for Real Time Online Model Learning. NIPS 2008: 1193-1200 - [c16]Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber:
Fitness Expectation Maximization. PPSN 2008: 337-346 - 2007
- [c15]Jan Peters, Stefan Schaal, Bernhard Schölkopf:
Towards Machine Learning of Motor Skills. AMS 2007: 138-144 - [c14]Jan Peters, Stefan Schaal:
Applying the Episodic Natural Actor-Critic Architecture to Motor Primitive Learning. ESANN 2007: 295-300 - [c13]Daan Wierstra, Alexander Förster, Jan Peters, Jürgen Schmidhuber:
Solving Deep Memory POMDPs with Recurrent Policy Gradients. ICANN (1) 2007: 697-706 - [c12]Jan Peters, Stefan Schaal:
Reinforcement learning by reward-weighted regression for operational space control. ICML 2007: 745-750 - [c11]Jan Peters, Stefan Schaal:
Policy Learning for Motor Skills. ICONIP (2) 2007: 233-242 - [c10]Jan Peters, Stefan Schaal:
Reinforcement Learning for Operational Space Control. ICRA 2007: 2111-2116 - [c9]Jun Nakanishi, Michael N. Mistry, Jan Peters, Stefan Schaal:
Towards compliant humanoids-an experimental assessment of suitable task space position/orientation controllers. IROS 2007: 2520-2527 - 2006
- [c8]Jan Peters, Stefan Schaal:
Reinforcement Learning for Parameterized Motor Primitives. IJCNN 2006: 73-80 - [c7]Jan Peters, Stefan Schaal:
Policy Gradient Methods for Robotics. IROS 2006: 2219-2225 - [c6]Jan Peters, Stefan Schaal:
Learning Operational Space Control. Robotics: Science and Systems 2006 - [c5]Jo-Anne Ting, Michael N. Mistry, Jan Peters, Stefan Schaal, Jun Nakanishi:
A Bayesian Approach to Nonlinear Parameter Identification for Rigid Body Dynamics. Robotics: Science and Systems 2006 - 2005
- [c4]Jan Peters, Sethu Vijayakumar, Stefan Schaal:
Natural Actor-Critic. ECML 2005: 280-291 - [c3]Jan Peters, Michael N. Mistry, Firdaus E. Udwadia, Rick Cory, Jun Nakanishi, Stefan Schaal:
A unifying methodology for the control of robotic systems. IROS 2005: 1824-1831 - [c2]Jun Nakanishi, Rick Cory, Michael N. Mistry, Jan Peters, Stefan Schaal:
Comparative experiments on task space control with redundancy resolution. IROS 2005: 3901-3908 - 2003
- [c1]Stefan Schaal, Jan Peters, Jun Nakanishi, Auke Jan Ijspeert:
Learning Movement Primitives. ISRR 2003: 561-572
Parts in Books or Collections
- 2016
- [p6]Jan Peters, Daniel D. Lee, Jens Kober, Duy Nguyen-Tuong, J. Andrew Bagnell, Stefan Schaal:
Robot Learning. Springer Handbook of Robotics, 2nd Ed. 2016: 357-398 - 2012
- [p5]Jens Kober, Jan Peters:
Reinforcement Learning in Robotics: A Survey. Reinforcement Learning 2012: 579-610 - 2010
- [p4]Olivier Sigaud, Jan Peters:
From Motor Learning to Interaction Learning in Robots. From Motor Learning to Interaction Learning in Robots 2010: 1-12 - [p3]Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters:
Real-Time Local GP Model Learning. From Motor Learning to Interaction Learning in Robots 2010: 193-207 - [p2]Jens Kober, Betty J. Mohler, Jan Peters:
Imitation and Reinforcement Learning for Motor Primitives with Perceptual Coupling. From Motor Learning to Interaction Learning in Robots 2010: 209-225 - [p1]Renaud Detry, Emre Baseski, Mila Popovic, Younes Touati, Norbert Krüger, Oliver Kroemer, Jan Peters, Justus H. Piater:
Learning Continuous Grasp Affordances by Sensorimotor Exploration. From Motor Learning to Interaction Learning in Robots 2010: 451-465
Editorship
- 2012
- [e3]Marc Peter Deisenroth, Csaba Szepesvári, Jan Peters:
Proceedings of the Tenth European Workshop on Reinforcement Learning, EWRL 2012, Edinburgh, Scotland, UK, June, 2012. JMLR Proceedings 24, JMLR.org 2012 [contents] - 2010
- [e2]Olivier Sigaud, Jan Peters:
From Motor Learning to Interaction Learning in Robots. Studies in Computational Intelligence 264, Springer 2010, ISBN 978-3-642-05180-7 [contents] - 2009
- [e1]Michael Beetz, Oliver Brock, Gordon Cheng, Jan Peters:
Cognition, Control and Learning for Robot Manipulation in Human Environments, 16.08. - 21.08.2009. Dagstuhl Seminar Proceedings 09341, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany 2009 [contents]
Reference Works
- 2017
- [r4]Jan Peters, J. Andrew Bagnell:
Policy Gradient Methods. Encyclopedia of Machine Learning and Data Mining 2017: 982-985 - [r3]Jan Peters, Russ Tedrake, Nick Roy, Jun Morimoto:
Robot Learning. Encyclopedia of Machine Learning and Data Mining 2017: 1106-1109 - 2010
- [r2]Jan Peters, J. Andrew Bagnell:
Policy Gradient Methods. Encyclopedia of Machine Learning 2010: 774-776 - [r1]Jan Peters, Russ Tedrake, Nicholas Roy, Jun Morimoto:
Robot Learning. Encyclopedia of Machine Learning 2010: 865-869
Informal and Other Publications
- 2024
- [i184]Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters:
Sharing Knowledge in Multi-Task Deep Reinforcement Learning. CoRR abs/2401.09561 (2024) - [i183]Duy M. H. Nguyen, Nina Lukashina, Tai Nguyen, An T. Le, TrungTin Nguyen, Nhat Ho, Jan Peters, Daniel Sonntag, Viktor Zaverkin, Mathias Niepert:
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks. CoRR abs/2402.01975 (2024) - [i182]Yasemin Göksu, Antonio De Almeida Correia, Vignesh Prasad, Alap Kshirsagar, Dorothea Koert, Jan Peters, Georgia Chalvatzaki:
Kinematically Constrained Human-like Bimanual Robot-to-Human Handovers. CoRR abs/2402.14525 (2024) - [i181]Fabian Hahne, Vignesh Prasad, Alap Kshirsagar, Dorothea Koert, Ruth Maria Stock-Homburg, Jan Peters, Georgia Chalvatzaki:
Transition State Clustering for Interaction Segmentation and Learning. CoRR abs/2402.14548 (2024) - [i180]Alessandro G. Bottero, Carlos E. Luis, Julia Vinogradska, Felix Berkenkamp, Jan Peters:
Information-Theoretic Safe Bayesian Optimization. CoRR abs/2402.15347 (2024) - [i179]Théo Vincent, Daniel Palenicek, Boris Belousov, Jan Peters, Carlo D'Eramo:
Iterated Q-Network: Beyond the One-Step Bellman Operator. CoRR abs/2403.02107 (2024) - [i178]Alina Böhm, Tim Schneider, Boris Belousov, Alap Kshirsagar, Lisa Pui Yee Lin, Katja Doerschner, Knut Drewing, Constantin A. Rothkopf, Jan Peters:
What Matters for Active Texture Recognition With Vision-Based Tactile Sensors. CoRR abs/2403.13701 (2024) - 2023
- [i177]Filip Bjelonic, Joonho Lee, Philip Arm, Dhionis V. Sako, Davide Tateo, Jan Peters, Marco Hutter:
Learning-based Design and Control for Quadrupedal Robots with Parallel-Elastic Actuators. CoRR abs/2301.03509 (2023) - [i176]Piotr Kicki, Puze Liu, Davide Tateo, Haitham Bou-Ammar, Krzysztof Walas, Piotr Skrzypczynski, Jan Peters:
Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks. CoRR abs/2301.04330 (2023) - [i175]Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters:
Model-Based Uncertainty in Value Functions. CoRR abs/2302.12526 (2023) - [i174]Shangding Gu, Alap Kshirsagar, Yali Du, Guang Chen, Yaodong Yang, Jan Peters, Alois C. Knoll:
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors. CoRR abs/2302.13137 (2023) - [i173]Firas Al-Hafez, Davide Tateo, Oleg Arenz, Guoping Zhao, Jan Peters:
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning. CoRR abs/2303.00599 (2023) - [i172]Daniel Palenicek, Michael Lutter, Joao Carvalho, Jan Peters:
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning. CoRR abs/2303.03955 (2023) - [i171]Johanna Bethge, Maik Pfefferkorn, Alexander Rose, Jan Peters, Rolf Findeisen:
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles. CoRR abs/2303.04725 (2023) - [i170]Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters:
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. CoRR abs/2305.01773 (2023) - [i169]Jihao Andreas Lin, Joe Watson, Pascal Klink, Jan Peters:
Function-Space Regularization for Deep Bayesian Classification. CoRR abs/2307.06055 (2023) - [i168]João Carvalho, An T. Le, Mark Baierl, Dorothea Koert, Jan Peters:
Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models. CoRR abs/2308.01557 (2023) - [i167]Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters:
Value-Distributional Model-Based Reinforcement Learning. CoRR abs/2308.06590 (2023) - [i166]Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters:
Sampling-Free Probabilistic Deep State-Space Models. CoRR abs/2309.08256 (2023) - [i165]Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
On the Benefit of Optimal Transport for Curriculum Reinforcement Learning. CoRR abs/2309.14091 (2023) - [i164]Pascal Klink, Florian Wolf, Kai Ploeger, Jan Peters, Joni Pajarinen:
Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning. CoRR abs/2309.14096 (2023) - [i163]Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters:
Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures. CoRR abs/2309.14298 (2023) - [i162]An T. Le, Georgia Chalvatzaki, Armin Biess, Jan Peters:
Accelerating Motion Planning via Optimal Transport. CoRR abs/2309.15970 (2023) - [i161]Aryaman Reddi, Maximilian Tölle, Jan Peters, Georgia Chalvatzaki, Carlo D'Eramo:
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula. CoRR abs/2311.01642 (2023) - [i160]Gabriele Tiboni, Pascal Klink, Jan Peters, Tatiana Tommasi, Carlo D'Eramo, Georgia Chalvatzaki:
Domain Randomization via Entropy Maximization. CoRR abs/2311.01885 (2023) - [i159]Firas Al-Hafez, Guoping Zhao, Jan Peters, Davide Tateo:
LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion. CoRR abs/2311.02496 (2023) - [i158]Firas Al-Hafez, Guoping Zhao, Jan Peters, Davide Tateo:
Time-Efficient Reinforcement Learning with Stochastic Stateful Policies. CoRR abs/2311.04082 (2023) - [i157]Luca Lach, Robert Haschke, Davide Tateo, Jan Peters, Helge J. Ritter, Júlia Borràs Sol, Carme Torras:
Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot. CoRR abs/2311.07245 (2023) - [i156]Ahmed Hendawy, Jan Peters, Carlo D'Eramo:
Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts. CoRR abs/2311.11385 (2023) - [i155]Vignesh Prasad, Lea Heitlinger, Dorothea Koert, Ruth Stock-Homburg, Jan Peters, Georgia Chalvatzaki:
Learning Multimodal Latent Dynamics for Human-Robot Interaction. CoRR abs/2311.16380 (2023) - [i154]Theo Gruner, Boris Belousov, Fabio Muratore, Daniel Palenicek, Jan Peters:
Pseudo-Likelihood Inference. CoRR abs/2311.16656 (2023) - [i153]Niklas Funk, Erik Helmut, Georgia Chalvatzaki, Roberto Calandra, Jan Peters:
Evetac: An Event-based Optical Tactile Sensor for Robotic Manipulation. CoRR abs/2312.01236 (2023) - [i152]Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters:
Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization. CoRR abs/2312.04386 (2023) - [i151]Cedric Derstroff, Mattia Cerrato, Jannis Brugger, Jan Peters, Stefan Kramer:
Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations. CoRR abs/2312.09950 (2023) - [i150]Théo Vincent, Alberto Maria Metelli, Boris Belousov, Jan Peters, Marcello Restelli, Carlo D'Eramo:
Parameterized Projected Bellman Operator. CoRR abs/2312.12869 (2023) - 2022
- [i149]Tianyu Ren, Alexander Imani Cowen-Rivers, Haitham Bou-Ammar, Jan Peters:
Learning Geometric Constraints in Task and Motion Planning. CoRR abs/2201.09612 (2022) - [i148]Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search. CoRR abs/2202.07071 (2022) - [i147]Bang You, Oleg Arenz, Youping Chen, Jan Peters:
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from Images. CoRR abs/2203.01810 (2022) - [i146]Stefan Löckel, Siwei Ju, Maximilian Schaller, Peter van Vliet, Jan Peters:
An Adaptive Human Driver Model for Realistic Race Car Simulations. CoRR abs/2203.01909 (2022) - [i145]Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters:
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits. CoRR abs/2203.03303 (2022) - [i144]João Carvalho, Jan Peters:
An Analysis of Measure-Valued Derivatives for Policy Gradients. CoRR abs/2203.03917 (2022) - [i143]João Carvalho, Dorothea Koert, Marek Daniv, Jan Peters:
Residual Robot Learning for Object-Centric Probabilistic Movement Primitives. CoRR abs/2203.03918 (2022) - [i142]Jascha Hellwig, Mark Baierl, João Carvalho, Julen Urain, Jan Peters:
A Hierarchical Approach to Active Pose Estimation. CoRR abs/2203.03919 (2022) - [i141]Snehal Jauhri, Jan Peters, Georgia Chalvatzaki:
Robot Learning of Mobile Manipulation with Reachability Behavior Priors. CoRR abs/2203.04051 (2022) - [i140]Niklas Funk, Svenja Menzenbach, Georgia Chalvatzaki, Jan Peters:
Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery. CoRR abs/2203.04120 (2022) - [i139]Puze Liu, Kuo Zhang, Davide Tateo, Snehal Jauhri, Jan Peters, Georgia Chalvatzaki:
Regularized Deep Signed Distance Fields for Reactive Motion Generation. CoRR abs/2203.04739 (2022) - [i138]Marius Memmel, Puze Liu, Davide Tateo, Jan Peters:
Dimensionality Reduction and Prioritized Exploration for Policy Search. CoRR abs/2203.04791 (2022) - [i137]Lei Xu, Tianyu Ren, Georgia Chalvatzaki, Jan Peters:
Accelerating Integrated Task and Motion Planning with Neural Feasibility Checking. CoRR abs/2203.10568 (2022) - [i136]Daniel Palenicek, Michael Lutter, Jan Peters:
Revisiting Model-based Value Expansion. CoRR abs/2203.14660 (2022) - [i135]Alexander Lambert, An T. Le, Julen Urain, Georgia Chalvatzaki, Byron Boots, Jan Peters:
Learning Implicit Priors for Motion Optimization. CoRR abs/2204.05369 (2022) - [i134]Tim Schneider, Boris Belousov, Hany Abdulsamad, Jan Peters:
Active Inference for Robotic Manipulation. CoRR abs/2206.10313 (2022) - [i133]Kai Ploeger, Jan Peters:
Controlling the Cascade: Kinematic Planning for N-ball Toss Juggling. CoRR abs/2207.01414 (2022) - [i132]Tuan Dam, Georgia Chalvatzaki, Jan Peters, Joni Pajarinen:
Monte-Carlo Robot Path Planning. CoRR abs/2208.02673 (2022) - [i131]Julen Urain, Niklas Funk, Jan Peters, Georgia Chalvatzaki:
SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion. CoRR abs/2209.03855 (2022) - [i130]Alexander I. Cowen-Rivers, Philip John Gorinski, Aivar Sootla, Asif Khan, Furui Liu, Jun Wang, Jan Peters, Haitham Bou-Ammar:
Structured Q-learning For Antibody Design. CoRR abs/2209.04698 (2022) - [i129]Bang You, Jingming Xie, Youping Chen, Jan Peters, Oleg Arenz:
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning. CoRR abs/2209.05333 (2022) - [i128]Puze Liu, Kuo Zhang, Davide Tateo, Snehal Jauhri, Zhiyuan Hu, Jan Peters, Georgia Chalvatzaki:
Safe reinforcement learning of dynamic high-dimensional robotic tasks: navigation, manipulation, interaction. CoRR abs/2209.13308 (2022) - [i127]Luca Lach, Niklas Funk, Robert Haschke, Séverin Lemaignan, Helge Joachim Ritter, Jan Peters, Georgia Chalvatzaki:
Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing. CoRR abs/2210.02054 (2022) - [i126]Joe Watson, Jan Peters:
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes. CoRR abs/2210.03512 (2022) - [i125]Kay Hansel, Julen Urain, Jan Peters, Georgia Chalvatzaki:
Hierarchical Policy Blending as Inference for Reactive Robot Control. CoRR abs/2210.07890 (2022) - [i124]Vignesh Prasad, Dorothea Koert, Ruth Stock-Homburg, Jan Peters, Georgia Chalvatzaki:
MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction. CoRR abs/2210.12418 (2022) - [i123]Tim Schneider, Boris Belousov, Georgia Chalvatzaki, Diego Romeres, Devesh K. Jha, Jan Peters:
Active Exploration for Robotic Manipulation. CoRR abs/2210.12806 (2022) - [i122]Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters:
Variational Hierarchical Mixtures for Learning Probabilistic Inverse Dynamics. CoRR abs/2211.01120 (2022) - [i121]Max Siebenborn, Boris Belousov, Junning Huang, Jan Peters:
How Crucial is Transformer in Decision Transformer? CoRR abs/2211.14655 (2022) - [i120]Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters:
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison. CoRR abs/2211.16110 (2022) - [i119]An T. Le, Kay Hansel, Jan Peters, Georgia Chalvatzaki:
Hierarchical Policy Blending As Optimal Transport. CoRR abs/2212.01938 (2022) - [i118]Alessandro G. Bottero, Carlos E. Luis, Julia Vinogradska, Felix Berkenkamp, Jan Peters:
Information-Theoretic Safe Exploration with Gaussian Processes. CoRR abs/2212.04914 (2022) - [i117]Yaonan Zhu, Shukrullo Nazirjonov, Bingheng Jiang, Jacinto E. Colan Zaita, Tadayoshi Aoyama, Yasuhisa Hasegawa, Boris Belousov, Kay Hansel, Jan Peters:
Visual Tactile Sensor Based Force Estimation for Position-Force Teleoperation. CoRR abs/2212.13007 (2022) - 2021
- [i116]Vignesh Prasad, Ruth Stock-Homburg, Jan Peters:
Human-Robot Handshaking: A Review. CoRR abs/2102.07193 (2021) - [i115]Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning. CoRR abs/2102.13176 (2021) - [i114]Vignesh Prasad, Ruth Stock-Homburg, Jan Peters:
Learning Human-like Hand Reaching for Human-Robot Handshaking. CoRR abs/2103.00616 (2021) - [i113]Tianyu Ren, Georgia Chalvatzaki, Jan Peters:
Extended Task and Motion Planning of Long-horizon Robot Manipulation. CoRR abs/2103.05456 (2021) - [i112]Joe Watson, Jan Peters:
Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk. CoRR abs/2103.06319 (2021) - [i111]Andrew S. Morgan, Daljeet Nandha, Georgia Chalvatzaki, Carlo D'Eramo, Aaron M. Dollar, Jan Peters:
Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning. CoRR abs/2103.13842 (2021) - [i110]Daniel Tanneberg, Kai Ploeger, Elmar Rueckert, Jan Peters:
SKID RAW: Skill Discovery from Raw Trajectories. CoRR abs/2103.14610 (2021) - [i109]Hany Abdulsamad, Tim Dorau, Boris Belousov, Jia-Jie Zhu, Jan Peters:
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative-Entropy Trust Regions. CoRR abs/2103.15388 (2021) - [i108]Stephan Weigand, Pascal Klink, Jan Peters, Joni Pajarinen:
Reinforcement Learning using Guided Observability. CoRR abs/2104.10986 (2021) - [i107]Niklas Funk, Charles B. Schaff, Rishabh Madan, Takuma Yoneda, Julen Urain De Jesus, Joe Watson, Ethan K. Gordon, Felix Widmaier, Stefan Bauer, Siddhartha S. Srinivasa, Tapomayukh Bhattacharjee, Matthew R. Walter, Jan Peters:
Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation. CoRR abs/2105.02087 (2021) - [i106]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Value Iteration in Continuous Actions, States and Time. CoRR abs/2105.04682 (2021) - [i105]Julen Urain, Anqi Li, Puze Liu, Carlo D'Eramo, Jan Peters:
Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning. CoRR abs/2105.04962 (2021) - [i104]Joe Watson, Hany Abdulsamad, Rolf Findeisen, Jan Peters:
Stochastic Control through Approximate Bayesian Input Inference. CoRR abs/2105.07693 (2021) - [i103]Daniel Tanneberg, Elmar Rueckert, Jan Peters:
Evolutionary Training and Abstraction Yields Algorithmic Generalization of Neural Computers. CoRR abs/2105.07957 (2021) - [i102]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Robust Value Iteration for Continuous Control Tasks. CoRR abs/2105.12189 (2021) - [i101]Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval, Ryan-Rhys Griffiths, Alexander I. Cowen-Rivers, Lin Yang, Lin Zhu, Wenlong Lyu, Zhitang Chen, Jun Wang, Jan Peters, Haitham Bou-Ammar:
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning. CoRR abs/2106.03609 (2021) - [i100]Puze Liu, Davide Tateo, Haitham Bou-Ammar, Jan Peters:
Efficient and Reactive Planning for High Speed Robot Air Hockey. CoRR abs/2107.06140 (2021) - [i99]João Carvalho, Davide Tateo, Fabio Muratore, Jan Peters:
An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients. CoRR abs/2107.09359 (2021) - [i98]Stefan Bauer, Felix Widmaier, Manuel Wüthrich, Niklas Funk, Julen Urain De Jesus, Jan Peters, Joe Watson, Claire Chen, Krishnan Srinivasan, Junwu Zhang, Jeffrey Zhang, Matthew R. Walter, Rishabh Madan, Charles B. Schaff, Takahiro Maeda, Takuma Yoneda, Denis Yarats, Arthur Allshire, Ethan K. Gordon, Tapomayukh Bhattacharjee, Siddhartha S. Srinivasa, Animesh Garg, Annika Buchholz, Sebastian Stark, Thomas Steinbrenner, Joel Akpo, Shruti Joshi, Vaibhav Agrawal, Bernhard Schölkopf:
A Robot Cluster for Reproducible Research in Dexterous Manipulation. CoRR abs/2109.10957 (2021) - [i97]Michael Lutter, Jan Peters:
Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models. CoRR abs/2110.01894 (2021) - [i96]Michael Lutter, Boris Belousov, Shie Mannor, Dieter Fox, Animesh Garg, Jan Peters:
Continuous-Time Fitted Value Iteration for Robust Policies. CoRR abs/2110.01954 (2021) - [i95]Julen Urain, Davide Tateo, Jan Peters:
Learning Stable Vector Fields on Lie Groups. CoRR abs/2110.11774 (2021) - [i94]Michael Lutter, Johannes Silberbauer, Joe Watson, Jan Peters:
A Differentiable Newton-Euler Algorithm for Real-World Robotics. CoRR abs/2110.12422 (2021) - [i93]Fabio Muratore, Fabio Ramos, Greg Turk, Wenhao Yu, Michael Gienger, Jan Peters:
Robot Learning from Randomized Simulations: A Review. CoRR abs/2111.00956 (2021) - [i92]Hany Abdulsamad, Jan Peters:
Model-Based Reinforcement Learning for Stochastic Hybrid Systems. CoRR abs/2111.06211 (2021) - [i91]Julien Brosseit, Benedikt Hahner, Fabio Muratore, Michael Gienger, Jan Peters:
Distilled Domain Randomization. CoRR abs/2112.03149 (2021) - 2020
- [i90]Simone Parisi, Davide Tateo, Maximilian Hensel, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning. CoRR abs/2001.00119 (2020) - [i89]Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters:
MushroomRL: Simplifying Reinforcement Learning Research. CoRR abs/2001.01102 (2020) - [i88]Samuele Tosatto, João Carvalho, Hany Abdulsamad, Jan Peters:
A Nonparametric Offpolicy Policy Gradient. CoRR abs/2001.02435 (2020) - [i87]Stefan Löckel, Jan Peters, Peter van Vliet:
A Probabilistic Framework for Imitating Human Race Driver Behavior. CoRR abs/2001.08255 (2020) - [i86]Ruth Stock-Homburg, Jan Peters, Katharina Schneider, Vignesh Prasad, Lejla Nukovic:
Evaluation of the Handshake Turing Test for anthropomorphic Robots. CoRR abs/2001.10464 (2020) - [i85]Samuele Tosatto, Riad Akrour, Jan Peters:
An Upper Bound of the Bias of Nadaraya-Watson Kernel Regression under Lipschitz Assumptions. CoRR abs/2001.10972 (2020) - [i84]Joni Pajarinen, Oleg Arenz, Jan Peters, Gerhard Neumann:
Probabilistic approach to physical object disentangling. CoRR abs/2002.11495 (2020) - [i83]Christian Eilers, Jonas Eschmann, Robin Menzenbach, Boris Belousov, Fabio Muratore, Jan Peters:
Underactuated Waypoint Trajectory Optimization for Light Painting Photography. CoRR abs/2003.01554 (2020) - [i82]Fabio Muratore, Christian Eilers, Michael Gienger, Jan Peters:
Bayesian Domain Randomization for Sim-to-Real Transfer. CoRR abs/2003.02471 (2020) - [i81]Samuele Tosatto, Jonas Stadtmueller, Jan Peters:
Dimensionality Reduction of Movement Primitives in Parameter Space. CoRR abs/2003.02634 (2020) - [i80]Marcus Ebner von Eschenbach, Binyamin Manela, Jan Peters, Armin Biess:
Metric-Based Imitation Learning Between Two Dissimilar Anthropomorphic Robotic Arms. CoRR abs/2003.02638 (2020) - [i79]Melvin Laux, Oleg Arenz, Jan Peters, Joni Pajarinen:
Deep Adversarial Reinforcement Learning for Object Disentangling. CoRR abs/2003.03779 (2020) - [i78]Philip Becker-Ehmck, Maximilian Karl, Jan Peters, Patrick van der Smagt:
Learning to Fly via Deep Model-Based Reinforcement Learning. CoRR abs/2003.08876 (2020) - [i77]Andrea Cini, Carlo D'Eramo, Jan Peters, Cesare Alippi:
Deep Reinforcement Learning with Weighted Q-Learning. CoRR abs/2003.09280 (2020) - [i76]Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Self-Paced Deep Reinforcement Learning. CoRR abs/2004.11812 (2020) - [i75]Hany Abdulsamad, Jan Peters:
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation. CoRR abs/2005.01432 (2020) - [i74]Nikolaos Gkanatsios, Georgia Chalvatzaki, Petros Maragos, Jan Peters:
Orientation Attentive Robot Grasp Synthesis. CoRR abs/2006.05123 (2020) - [i73]Riad Akrour, Davide Tateo, Jan Peters:
Reinforcement Learning from a Mixture of Interpretable Experts. CoRR abs/2006.05911 (2020) - [i72]Dieter Büchler, Simon Guist, Roberto Calandra, Vincent Berenz, Bernhard Schölkopf, Jan Peters:
Learning to Play Table Tennis From Scratch using Muscular Robots. CoRR abs/2006.05935 (2020) - [i71]Andreas Look, Chen Qiu, Maja Rudolph, Jan Peters, Melih Kandemir:
Deterministic Inference of Neural Stochastic Differential Equations. CoRR abs/2006.08973 (2020) - [i70]Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Convex Regularization in Monte-Carlo Tree Search. CoRR abs/2007.00391 (2020) - [i69]Mikko Lauri, Joni Pajarinen, Jan Peters, Simone Frintrop:
Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization. CoRR abs/2007.02084 (2020) - [i68]Leon Keller, Daniel Tanneberg, Svenja Stark, Jan Peters:
Model-Based Quality-Diversity Search for Efficient Robot Learning. CoRR abs/2008.04589 (2020) - [i67]Marco Ewerton, Oleg Arenz, Jan Peters:
Assisted Teleoperation in Changing Environments with a Mixture of Virtual Guides. CoRR abs/2008.05251 (2020) - [i66]Vignesh Prasad, Ruth Stock-Homburg, Jan Peters:
Advances in Human-Robot Handshaking. CoRR abs/2008.11695 (2020) - [i65]Joe Watson, Abraham Imohiosen, Jan Peters:
Active Inference or Control as Inference? A Unifying View. CoRR abs/2010.00262 (2020) - [i64]Andreas Look, Simona Doneva, Melih Kandemir, Rainer Gemulla, Jan Peters:
Differentiable Implicit Layers. CoRR abs/2010.07078 (2020) - [i63]Michael Lutter, Johannes Silberbauer, Joe Watson, Jan Peters:
A Differentiable Newton Euler Algorithm for Multi-body Model Learning. CoRR abs/2010.09802 (2020) - [i62]Julen Urain, Michele Ginesi, Davide Tateo, Jan Peters:
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows. CoRR abs/2010.13129 (2020) - [i61]Kai Ploeger, Michael Lutter, Jan Peters:
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards. CoRR abs/2010.13483 (2020) - [i60]Samuele Tosatto, Georgia Chalvatzaki, Jan Peters:
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills. CoRR abs/2010.13766 (2020) - [i59]Samuele Tosatto, João Carvalho, Jan Peters:
Batch Reinforcement Learning with a Nonparametric Off-Policy Policy Gradient. CoRR abs/2010.14771 (2020) - [i58]Michael Lutter, Johannes Silberbauer, Joe Watson, Jan Peters:
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning. CoRR abs/2011.01734 (2020) - [i57]Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters:
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning. CoRR abs/2011.05217 (2020) - [i56]Riad Akrour, Asma Atamna, Jan Peters:
Convex Optimization with an Interpolation-based Projection and its Application to Deep Learning. CoRR abs/2011.07016 (2020) - [i55]Sebastian Höfer, Kostas E. Bekris, Ankur Handa, Juan Camilo Gamboa Higuera, Florian Golemo, Melissa Mozifian, Christopher G. Atkeson, Dieter Fox, Ken Goldberg, John Leonard, C. Karen Liu, Jan Peters, Shuran Song, Peter Welinder, Martha White:
Perspectives on Sim2Real Transfer for Robotics: A Summary of the R: SS 2020 Workshop. CoRR abs/2012.03806 (2020) - [i54]Julen Urain, Davide Tateo, Tianyu Ren, Jan Peters:
Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems. CoRR abs/2012.06224 (2020) - 2019
- [i53]Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya:
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos. CoRR abs/1902.01240 (2019) - [i52]Joni Pajarinen, Hong Linh Thai, Riad Akrour, Jan Peters, Gerhard Neumann:
Compatible Natural Gradient Policy Search. CoRR abs/1902.02823 (2019) - [i51]Diego Agudelo-España, Sebastián Gómez-González, Stefan Bauer, Bernhard Schölkopf, Jan Peters:
Bayesian Online Detection and Prediction of Change Points. CoRR abs/1902.04524 (2019) - [i50]Aditya Bhatt, Daniel Palenicek, Boris Belousov, Max Argus, Artemij Amiranashvili, Thomas Brox, Jan Peters:
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity. CoRR abs/1902.05605 (2019) - [i49]Mikko Lauri, Joni Pajarinen, Jan Peters:
Information Gathering in Decentralized POMDPs by Policy Graph Improvement. CoRR abs/1902.09840 (2019) - [i48]Kristian Kersting, Jan Peters, Constantin A. Rothkopf:
Was ist eine Professur fuer Kuenstliche Intelligenz? CoRR abs/1903.09516 (2019) - [i47]Dieter Büchler, Roberto Calandra, Jan Peters:
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots. CoRR abs/1904.03665 (2019) - [i46]Zinan Liu, Kai Ploeger, Svenja Stark, Elmar Rueckert, Jan Peters:
Learning walk and trot from the same objective using different types of exploration. CoRR abs/1904.12336 (2019) - [i45]Philip Becker-Ehmck, Jan Peters, Patrick van der Smagt:
Switching Linear Dynamics for Variational Bayes Filtering. CoRR abs/1905.12434 (2019) - [i44]Zinan Liu, Arne Hitzmann, Shuhei Ikemoto, Svenja Stark, Jan Peters, Koh Hosoda:
Local Online Motor Babbling: Learning Motor Abundance of A Musculoskeletal Robot Arm. CoRR abs/1906.09013 (2019) - [i43]David Nass, Boris Belousov, Jan Peters:
Entropic Risk Measure in Policy Search. CoRR abs/1906.09090 (2019) - [i42]Julen Urain, Jan Peters:
Generalized Multiple Correlation Coefficient as a Similarity Measurement between Trajectories. CoRR abs/1906.09802 (2019) - [i41]Onur Celik, Hany Abdulsamad, Jan Peters:
Chance-Constrained Trajectory Optimization for Non-linear Systems with Unknown Stochastic Dynamics. CoRR abs/1906.11003 (2019) - [i40]Susanne Trick, Dorothea Koert, Jan Peters, Constantin A. Rothkopf:
Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction. CoRR abs/1907.02426 (2019) - [i39]Boris Belousov, Jan Peters:
Entropic Regularization of Markov Decision Processes. CoRR abs/1907.04214 (2019) - [i38]Michael Lutter, Kim Listmann, Jan Peters:
Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems. CoRR abs/1907.04489 (2019) - [i37]Michael Lutter, Christian Ritter, Jan Peters:
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning. CoRR abs/1907.04490 (2019) - [i36]Fabio Muratore, Michael Gienger, Jan Peters:
Assessing Transferability from Simulation to Reality for Reinforcement Learning. CoRR abs/1907.04685 (2019) - [i35]Svenja Stark, Jan Peters, Elmar Rueckert:
Experience Reuse with Probabilistic Movement Primitives. CoRR abs/1908.03936 (2019) - [i34]Zhang-Wei Hong, Joni Pajarinen, Jan Peters:
Model-based Lookahead Reinforcement Learning. CoRR abs/1908.06012 (2019) - [i33]Sebastián Gómez-González, Yassine Nemmour, Bernhard Schölkopf, Jan Peters:
Reliable Real Time Ball Tracking for Robot Table Tennis. CoRR abs/1908.07332 (2019) - [i32]Sebastián Gómez-González, Sergey Prokudin, Bernhard Schölkopf, Jan Peters:
Real Time Trajectory Prediction Using Deep Conditional Generative Models. CoRR abs/1909.03895 (2019) - [i31]Michael Lutter, Boris Belousov, Kim Listmann, Debora Clever, Jan Peters:
HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints. CoRR abs/1909.06153 (2019) - [i30]Boris Belousov, Alymbek Sadybakasov, Bastian Wibranek, Filipe Veiga, Oliver Tessmann, Jan Peters:
Building a Library of Tactile Skills Based on FingerVision. CoRR abs/1909.09669 (2019) - [i29]Pascal Klink, Hany Abdulsamad, Boris Belousov, Jan Peters:
Self-Paced Contextual Reinforcement Learning. CoRR abs/1910.02826 (2019) - [i28]Joe Watson, Hany Abdulsamad, Jan Peters:
Stochastic Optimal Control as Approximate Input Inference. CoRR abs/1910.03003 (2019) - [i27]Matthias Schultheis, Boris Belousov, Hany Abdulsamad, Jan Peters:
Receding Horizon Curiosity. CoRR abs/1910.03620 (2019) - [i26]Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Generalized Mean Estimation in Monte-Carlo Tree Search. CoRR abs/1911.00384 (2019) - [i25]Daniel Tanneberg, Elmar Rueckert, Jan Peters:
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer. CoRR abs/1911.00926 (2019) - 2018
- [i24]Boris Belousov, Jan Peters:
f-Divergence constrained policy improvement. CoRR abs/1801.00056 (2018) - [i23]Daniel Tanneberg, Jan Peters, Elmar Rueckert:
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks. CoRR abs/1802.08013 (2018) - [i22]Adrian Sosic, Elmar Rueckert, Jan Peters, Abdelhak M. Zoubir, Heinz Koeppl:
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling. CoRR abs/1803.00444 (2018) - [i21]Filipe Veiga, Benoni B. Edin, Jan Peters:
In-Hand Object Stabilization by Independent Finger Control. CoRR abs/1806.05031 (2018) - [i20]Okan Koc, Guilherme Maeda, Jan Peters:
Optimizing Execution of Dynamic Goal-Directed Robot Movements with Learning Control. CoRR abs/1807.01918 (2018) - [i19]Sebastián Gómez-González, Gerhard Neumann, Bernhard Schölkopf, Jan Peters:
Adaptation and Robust Learning of Probabilistic Movement Primitives. CoRR abs/1808.10648 (2018) - [i18]Okan Koc, Jan Peters:
Learning to serve: an experimental study for a new learning from demonstrations framework. CoRR abs/1810.12950 (2018) - [i17]Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, Jan Peters:
An Algorithmic Perspective on Imitation Learning. CoRR abs/1811.06711 (2018) - [i16]Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan:
TD-Regularized Actor-Critic Methods. CoRR abs/1812.08288 (2018) - 2017
- [i15]Joni Pajarinen, Ville Kyrki, Michael C. Koval, Siddhartha S. Srinivasa, Jan Peters, Gerhard Neumann:
Hybrid control trajectory optimization under uncertainty. CoRR abs/1702.04396 (2017) - 2016
- [i14]Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama:
Policy Search with High-Dimensional Context Variables. CoRR abs/1611.03231 (2016) - [i13]Filipe Veiga, Jan Peters:
Can Modular Finger Control for In-Hand Object Stabilization be accomplished by Independent Tactile Feedback Control Laws? CoRR abs/1612.08202 (2016) - 2015
- [i12]Serena Ivaldi, Sébastien Lefort, Jan Peters, Mohamed Chetouani, Joelle Provasi, Elisabetta Zibetti:
Towards engagement models that consider individual factors in HRI: on the relation of extroversion and negative attitude towards robots to gaze and speech during a human-robot assembly task. CoRR abs/1508.04603 (2015) - [i11]Elmar Rueckert, Rudolf Lioutikov, Roberto Calandra, Marius Schmidt, Philipp Beckerle, Jan Peters:
Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations. CoRR abs/1510.03253 (2015) - [i10]Jan Peters, Justus H. Piater, Robert Platt Jr., Siddhartha S. Srinivasa:
Multimodal Manipulation Under Uncertainty (Dagstuhl Seminar 15411). Dagstuhl Reports 5(10): 1-18 (2015) - 2014
- [i9]Sanket Kamthe, Jan Peters, Marc Peter Deisenroth:
Multi-modal filtering for non-linear estimation. CoRR abs/1401.0077 (2014) - [i8]Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth:
Manifold Gaussian Processes for Regression. CoRR abs/1402.5876 (2014) - 2013
- [i7]Marc Peter Deisenroth, Peter Englert, Jan Peters, Dieter Fox:
Multi-Task Policy Search. CoRR abs/1307.0813 (2013) - 2012
- [i6]Jens Kober, Jan Peters:
Learning Prioritized Control of Motor Primitives. CoRR abs/1209.0488 (2012) - 2011
- [i5]Yevgeny Seldin, François Laviolette, John Shawe-Taylor, Jan Peters, Peter Auer:
PAC-Bayesian Analysis of Martingales and Multiarmed Bandits. CoRR abs/1105.2416 (2011) - [i4]Yevgeny Seldin, Nicolò Cesa-Bianchi, François Laviolette, Peter Auer, John Shawe-Taylor, Jan Peters:
PAC-Bayesian Analysis of the Exploration-Exploitation Trade-off. CoRR abs/1105.4585 (2011) - [i3]Jeremy L. Wyatt, Peter Dayan, Ales Leonardis, Jan Peters:
Exploration and Curiosity in Robot Learning and Inference (Dagstuhl Seminar 11131). Dagstuhl Reports 1(3): 67-95 (2011) - 2009
- [i2]Michael Beetz, Oliver Brock, Gordon Cheng, Jan Peters:
09341 Abstracts Collection - Cognition, Control and Learning for Robot Manipulation in Human Environments. Cognition, Control and Learning for Robot Manipulation in Human Environments 2009 - [i1]Michael Beetz, Oliver Brock, Gordon Cheng, Jan Peters:
09341 Summary - Cognition, Control and Learning for Robot Manipulation in Human Environments. Cognition, Control and Learning for Robot Manipulation in Human Environments 2009
Coauthor Index
João Carvalho
aka: Joao Carvalho
aka: Joao Carvalho
[j126] [j125] [c280] [c279] [c271] [c270] [c269] [c267] [c266] [c265] [c258] [c253] [c252] [c251] [c250] [c248] [c242] [c238] [c234] [c233] [i182] [i181] [i162] [i161] [i160] [i155] [i153] [i141] [i140] [i139] [i137] [i135] [i132] [i131] [i128] [i127] [i125] [i124] [i123] [i119] [i113] [i111] [i74] [i60]
Julen Urain De Jesus
aka: Julen Urain
aka: Julen Urain
Oliver Kroemer
aka: Oliver Krömer
aka: Oliver Krömer
[j99] [j96] [j91] [j87] [j83] [j82] [j81] [j78] [j77] [j69] [j68] [j65] [j64] [j62] [j59] [j57] [j54] [j53] [j52] [j45] [j44] [j43] [j39] [c202] [c185] [c184] [c183] [c182] [c181] [c173] [c172] [c170] [c169] [c167] [c165] [c163] [c162] [c158] [c157] [c150] [c149] [c145] [c144] [c143] [c142] [c140] [c136] [c135] [c134] [c133] [c130] [c128] [c124] [c123] [c122] [c121] [c119] [c116] [c113] [c110] [c109] [c108] [c103] [c101] [c99] [c96] [c94] [c92] [c91] [c83] [c82] [c75] [c40] [c18] [i84] [i52] [i19] [i17] [i15] [i14]
Ruth Stock-Homburg
aka: Ruth Maria Stock-Homburg
aka: Ruth Maria Stock-Homburg
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