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ORCIDJan Peters 0001
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
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2020 – today
- 2022
[j128]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)- [j127]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) - [j126]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) - [j125]Bang You, Oleg Arenz, Youping Chen, Jan Peters:
Integrating contrastive learning with dynamic models for reinforcement learning from images. Neurocomputing 476: 102-114 (2022) - [j124]Vignesh Prasad, Ruth Stock-Homburg, Jan Peters:
Human-Robot Handshaking: A Review. Int. J. Soc. Robotics 14(1): 277-293 (2022) - [j123]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) - [j122]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) - [c246]Marius Memmel, Puze Liu, Davide Tateo, Jan Peters:
Dimensionality Reduction and Prioritized Exploration for Policy Search. AISTATS 2022: 2134-2157 - [i130]Tianyu Ren, Alexander Imani Cowen-Rivers, Haitham Bou-Ammar, Jan Peters:
Learning Geometric Constraints in Task and Motion Planning. CoRR abs/2201.09612 (2022) - [i129]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) - [i128]Bang You, Oleg Arenz, Youping Chen, Jan Peters:
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from Images. CoRR abs/2203.01810 (2022) - [i127]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) - [i126]Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters:
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits. CoRR abs/2203.03303 (2022) - [i125]Joao Carvalho, Jan Peters:
An Analysis of Measure-Valued Derivatives for Policy Gradients. CoRR abs/2203.03917 (2022) - [i124]Joao Carvalho, Dorothea Koert, Marek Daniv, Jan Peters:
Residual Robot Learning for Object-Centric Probabilistic Movement Primitives. CoRR abs/2203.03918 (2022) - [i123]Jascha Hellwig, Mark Baierl, Joao Carvalho, Julen Urain, Jan Peters:
A Hierarchical Approach to Active Pose Estimation. CoRR abs/2203.03919 (2022) - [i122]Snehal Jauhri, Jan Peters, Georgia Chalvatzaki:
Robot Learning of Mobile Manipulation with Reachability Behavior Priors. CoRR abs/2203.04051 (2022) - [i121]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) - [i120]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) - [i119]Marius Memmel, Puze Liu, Davide Tateo, Jan Peters:
Dimensionality Reduction and Prioritized Exploration for Policy Search. CoRR abs/2203.04791 (2022) - [i118]Lei Xu, Tianyu Ren, Georgia Chalvatzaki, Jan Peters:
Accelerating Integrated Task and Motion Planning with Neural Feasibility Checking. CoRR abs/2203.10568 (2022) - [i117]Daniel Palenicek, Michael Lutter, Jan Peters:
Revisiting Model-based Value Expansion. CoRR abs/2203.14660 (2022) - [i116]Alexander Lambert, An T. Le, Julen Urain, Georgia Chalvatzaki, Byron Boots, Jan Peters:
Learning Implicit Priors for Motion Optimization. CoRR abs/2204.05369 (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) - [c245]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 - [c244]Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, Jan Peters:
Latent Derivative Bayesian Last Layer Networks. AISTATS 2021: 1198-1206 - [c243]Joe Watson, Jan Peters:
Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk. ACC 2021: 1231-1236 - [c242]Michael Lutter, Debora Clever, René Kirsten, Kim Listmann, Jan Peters:
Building Skill Learning Systems for Robotics. CASE 2021: 1878-1883 - [c241]Puze Liu, Davide Tateo, Haitham Bou-Ammar, Jan Peters:
Robot Reinforcement Learning on the Constraint Manifold. CoRL 2021: 1357-1366 - [c240]Niklas Funk, Georgia Chalvatzaki, Boris Belousov, Jan Peters:
Learn2Assemble with Structured Representations and Search for Robotic Architectural Construction. CoRL 2021: 1401-1411 - [c239]Fabio Muratore, Theo Gruner, Florian Wiese, Boris Belousov, Michael Gienger, Jan Peters:
Neural Posterior Domain Randomization. CoRL 2021: 1532-1542 - [c238]Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Convex Regularization in Monte-Carlo Tree Search. ICML 2021: 2365-2375 - [c237]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Value Iteration in Continuous Actions, States and Time. ICML 2021: 7224-7234 - [c236]Qin Li, Georgia Chalvatzaki, Jan Peters, Yong Wang:
Directed Acyclic Graph Neural Network for Human Motion Prediction. ICRA 2021: 3197-3204 - [c235]Vignesh Prasad, Ruth Stock-Homburg, Jan Peters:
Learning Human-like Hand Reaching for Human-Robot Handshaking. ICRA 2021: 3612-3618 - [c234]Michael Lutter, Johannes Silberbauer, Joe Watson, Jan Peters:
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning. ICRA 2021: 4163-4170 - [c233]Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters:
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning. ICRA 2021: 4216-4222 - [c232]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 - [c231]Samuele Tosatto, Georgia Chalvatzaki, Jan Peters:
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills. ICRA 2021: 10815-10821 - [c230]João Carvalho, Davide Tateo, Fabio Muratore, Jan Peters:
An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients. IJCNN 2021: 1-10 - [c229]Puze Liu, Davide Tateo, Haitham Bou-Ammar, Jan Peters:
Efficient and Reactive Planning for High Speed Robot Air Hockey. IROS 2021: 586-593 - [c228]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Robust Value Iteration for Continuous Control Tasks. Robotics: Science and Systems 2021 - [c227]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 - [i115]Vignesh Prasad, Ruth Stock-Homburg, Jan Peters:
Human-Robot Handshaking: A Review. CoRR abs/2102.07193 (2021) - [i114]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) - [i113]Vignesh Prasad, Ruth Stock-Homburg, Jan Peters:
Learning Human-like Hand Reaching for Human-Robot Handshaking. CoRR abs/2103.00616 (2021) - [i112]Tianyu Ren, Georgia Chalvatzaki, Jan Peters:
Extended Task and Motion Planning of Long-horizon Robot Manipulation. CoRR abs/2103.05456 (2021) - [i111]Joe Watson, Jan Peters:
Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk. CoRR abs/2103.06319 (2021) - [i110]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) - [i109]Daniel Tanneberg, Kai Ploeger, Elmar Rueckert, Jan Peters:
SKID RAW: Skill Discovery from Raw Trajectories. CoRR abs/2103.14610 (2021) - [i108]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) - [i107]Stephan Weigand, Pascal Klink, Jan Peters, Joni Pajarinen:
Reinforcement Learning using Guided Observability. CoRR abs/2104.10986 (2021) - [i106]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) - [i105]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Value Iteration in Continuous Actions, States and Time. CoRR abs/2105.04682 (2021) - [i104]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) - [i103]Joe Watson, Hany Abdulsamad, Rolf Findeisen, Jan Peters:
Stochastic Control through Approximate Bayesian Input Inference. CoRR abs/2105.07693 (2021) - [i102]Daniel Tanneberg, Elmar Rueckert, Jan Peters:
Evolutionary Training and Abstraction Yields Algorithmic Generalization of Neural Computers. CoRR abs/2105.07957 (2021) - [i101]Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg:
Robust Value Iteration for Continuous Control Tasks. CoRR abs/2105.12189 (2021) - [i100]Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval, Ryan-Rhys Griffiths, Alexander Imani 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) - [i99]Puze Liu, Davide Tateo, Haitham Bou-Ammar, Jan Peters:
Efficient and Reactive Planning for High Speed Robot Air Hockey. CoRR abs/2107.06140 (2021) - [i98]João Carvalho, Davide Tateo, Fabio Muratore, Jan Peters:
An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients. CoRR abs/2107.09359 (2021) - [i97]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) - [i96]Michael Lutter, Jan Peters:
Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models. CoRR abs/2110.01894 (2021) - [i95]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) - [i94]Julen Urain, Davide Tateo, Jan Peters:
Learning Stable Vector Fields on Lie Groups. CoRR abs/2110.11774 (2021) - [i93]Michael Lutter, Johannes Silberbauer, Joe Watson, Jan Peters:
A Differentiable Newton-Euler Algorithm for Real-World Robotics. CoRR abs/2110.12422 (2021) - [i92]Fabio Muratore, Fabio Ramos, Greg Turk, Wenhao Yu, Michael Gienger, Jan Peters:
Robot Learning from Randomized Simulations: A Review. CoRR abs/2111.00956 (2021) - [i91]Hany Abdulsamad, Jan Peters:
Model-Based Reinforcement Learning for Stochastic Hybrid Systems. CoRR abs/2111.06211 (2021) - [i90]Julien Brosseit, Benedikt Hahner, Fabio Muratore, Michael Gienger, Jan Peters:
Distilled Domain Randomization. CoRR abs/2112.03149 (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) - [c226]Samuele Tosatto, João Carvalho, Hany Abdulsamad, Jan Peters:
A Nonparametric Off-Policy Policy Gradient. AISTATS 2020: 167-177 - [c225]Kai Ploeger, Michael Lutter, Jan Peters:
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards. CoRL 2020: 642-653 - [c224]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 - [c223]Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters:
Sharing Knowledge in Multi-Task Deep Reinforcement Learning. ICLR 2020 - [c222]Christian Eilers, Jonas Eschmann, Robin Menzenbach, Boris Belousov, Fabio Muratore, Jan Peters:
Underactuated Waypoint Trajectory Optimization for Light Painting Photography. ICRA 2020: 1505-1510 - [c221]Christoph Zelch, Jan Peters, Oskar von Stryk:
Learning Control Policies from Optimal Trajectories. ICRA 2020: 2529-2535 - [c220]Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Generalized Mean Estimation in Monte-Carlo Tree Search. IJCAI 2020: 2397-2404 - [c219]Julen Urain, Michele Ginesi, Davide Tateo, Jan Peters:
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows. IROS 2020: 5231-5237 - [c218]Nils Rottmann, Tjasa Kunavar, Jan Babic, Jan Peters, Elmar Rueckert:
Learning Hierarchical Acquisition Functions for Bayesian Optimization. IROS 2020: 5490-5496 - [c217]Melvin Laux, Oleg Arenz, Jan Peters, Joni Pajarinen:
Deep Adversarial Reinforcement Learning for Object Disentangling. IROS 2020: 5504-5510 - [c216]Anton Ziese, Mario D. Fiore, Jan Peters, Uwe E. Zimmermann, Jürgen Adamy:
Redundancy resolution under hard joint constraints: a generalized approach to rank updates. IROS 2020: 7447-7453 - [c215]Leon Keller, Daniel Tanneberg, Svenja Stark, Jan Peters:
Model-Based Quality-Diversity Search for Efficient Robot Learning. IROS 2020: 9675-9680 - [c214]Allan Almeida Santos, Edwin Mora, Jan Peters, Florian Steinke:
Decentralized Data-Driven Tuning of Droop Frequency Controllers. ISGT-Europe 2020: 141-145 - [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 - [i89]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) - [i88]Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters:
MushroomRL: Simplifying Reinforcement Learning Research. CoRR abs/2001.01102 (2020) - [i87]Samuele Tosatto, João Carvalho, Hany Abdulsamad, Jan Peters:
A Nonparametric Offpolicy Policy Gradient. CoRR abs/2001.02435 (2020) - [i86]Stefan Löckel, Jan Peters, Peter van Vliet:
A Probabilistic Framework for Imitating Human Race Driver Behavior. CoRR abs/2001.08255 (2020) - [i85]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) - [i84]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) - [i83]Joni Pajarinen, Oleg Arenz, Jan Peters, Gerhard Neumann:
Probabilistic approach to physical object disentangling. CoRR abs/2002.11495 (2020) - [i82]