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ORCIDJens Kober
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- affiliation: Delft University of Technology, Delft, The Netherlands
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2020 – today
- 2023
[j28]Armin Avaei, Linda F. van der Spaa
, Luka Peternel, Jens Kober:
An Incremental Inverse Reinforcement Learning Approach for Motion Planning with Separated Path and Velocity Preferences. Robotics 12(2): 61 (2023)- [j27]Julian Frederik Schumann, Jens Kober, Arkady Zgonnikov:
Benchmarking Behavior Prediction Models in Gap Acceptance Scenarios. IEEE Trans. Intell. Veh. 8(3): 2580-2591 (2023) - [i30]Jihong Zhu, Michael Gienger, Giovanni Franzese, Jens Kober:
Do You Need a Hand? - a Bimanual Robotic Dressing Assistance Scheme. CoRR abs/2301.02749 (2023) - [i29]Armin Avaei, Linda F. van der Spaa, Luka Peternel, Jens Kober:
An Incremental Inverse Reinforcement Learning Approach for Motion Planning with Human Preferences. CoRR abs/2301.10528 (2023) - [i28]Rodrigo Pérez-Dattari, Jens Kober:
Stable Motion Primitives via Imitation and Contrastive Learning. CoRR abs/2302.10017 (2023) - [i27]Khaled A. Mustafa, Oscar de Groot, Xinwei Wang, Jens Kober, Javier Alonso-Mora:
Probabilistic Risk Assessment for Chance-Constrained Collision Avoidance in Uncertain Dynamic Environments. CoRR abs/2302.10846 (2023) - [i26]Yulei Qiu, Jihong Zhu, Cosimo Della Santina, Michael Gienger, Jens Kober:
Robotic Fabric Flattening with Wrinkle Direction Detection. CoRR abs/2303.04909 (2023) - [i25]Jianyong Sun, Jihong Zhu, Jens Kober, Michael Gienger:
Learning from Few Demonstrations with Frame-Weighted Motion Generation. CoRR abs/2303.14188 (2023) - [i24]Eveline Drijver, Rodrigo Pérez-Dattari, Jens Kober, Cosimo Della Santina, Zlatan Ajanovic:
Robotic Packaging Optimization with Reinforcement Learning. CoRR abs/2303.14693 (2023) - [i23]Anna Mészáros, Javier Alonso-Mora, Jens Kober:
TrajFlow: Learning the Distribution over Trajectories. CoRR abs/2304.05166 (2023) - [i22]Jari J. van Steen, Gijs van den Brandt, Nathan van de Wouw, Jens Kober, Alessandro Saccon:
Quadratic Programming-based Reference Spreading Control for Dual-Arm Robotic Manipulation with Planned Simultaneous Impacts. CoRR abs/2305.08643 (2023) - [i21]Julian Frederik Schumann, Aravinda Ramakrishnan Srinivasan, Jens Kober, Gustav Markkula, Arkady Zgonnikov:
Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study. CoRR abs/2305.15187 (2023) - 2022
- [j26]Padmaja Kulkarni, Jens Kober, Robert Babuska, Cosimo Della Santina:
Learning Assembly Tasks in a Few Minutes by Combining Impedance Control and Residual Recurrent Reinforcement Learning. Adv. Intell. Syst. 4(1) (2022) - [j25]Carlos Celemin, Rodrigo Pérez-Dattari, Eugenio Chisari, Giovanni Franzese, Leandro de Souza Rosa, Ravi Prakash, Zlatan Ajanovic, Marta Ferraz, Abhinav Valada, Jens Kober:
Interactive Imitation Learning in Robotics: A Survey. Found. Trends Robotics 10(1-2): 1-197 (2022) - [j24]Jihong Zhu, Michael Gienger, Jens Kober:
Learning Task-Parameterized Skills From Few Demonstrations. IEEE Robotics Autom. Lett. 7(2): 4063-4070 (2022) - [j23]Anna Mészáros, Giovanni Franzese, Jens Kober:
Learning to Pick at Non-Zero-Velocity From Interactive Demonstrations. IEEE Robotics Autom. Lett. 7(3): 6052-6059 (2022) - [j22]Jihong Zhu, Andrea Cherubini, Claire Dune, David Navarro-Alarcon, Farshid Alambeigi, Dmitry Berenson, Fanny Ficuciello, Kensuke Harada, Jens Kober, Xiang Li, Jia Pan, Wenzhen Yuan, Michael Gienger:
Challenges and Outlook in Robotic Manipulation of Deformable Objects. IEEE Robotics Autom. Mag. 29(3): 67-77 (2022) - [c49]Maaike G. Beuling, Tom C. T. van Riet, Jan van Frankenhuyzen, Reinier van Antwerpen, Bas de Blocq van Scheltinga, Arnout H. H. Dourleijn, Dzan Ireiz, Sander Streefkerk, Jonathan C. van Zanten Jan de Lange, Jens Kober, Dimitra Dodou:
Development and testing of a prototype of a dental extraction trainer with real-time feedback on forces, torques, and angular velocity. EMBC 2022: 3285-3290 - [c48]Nikolaos Passalis, S. Pedrazzi, Robert Babuska, Wolfram Burgard, D. Dias, F. Ferro, Moncef Gabbouj, O. Green, Alexandros Iosifidis, E. Kayacan, Jens Kober, O. Michel, Nikos Nikolaidis, Paraskevi Nousi, Roel Pieters, Maria Tzelepi, Abhinav Valada, Anastasios Tefas:
OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics. IROS 2022: 12479-12484 - [c47]Evelyn D'Elia, Jean-Baptiste Mouret, Jens Kober, Serena Ivaldi:
Automatic Tuning and Selection of Whole-Body Controllers. IROS 2022: 12935-12941 - [i20]Jihong Zhu, Michael Gienger, Jens Kober:
Learning Task-Parameterized Skills from Few Demonstrations. CoRR abs/2201.09975 (2022) - [i19]Nikolaos Passalis, S. Pedrazzi, Robert Babuska, Wolfram Burgard, D. Dias, F. Ferro, Moncef Gabbouj, O. Green, Alexandros Iosifidis, E. Kayacan, Jens Kober, O. Michel, Nikolaos Nikolaidis, Paraskevi Nousi, Roel Pieters, Maria Tzelepi, Abhinav Valada, Anastasios Tefas:
OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics. CoRR abs/2203.00403 (2022) - [i18]Antonin Raffin, Daniel Seidel, Jens Kober, Alin Albu-Schäffer, João Silvério, Freek Stulp:
Learning to Exploit Elastic Actuators for Quadruped Locomotion. CoRR abs/2209.07171 (2022) - [i17]Mariano Ramirez Montero, Giovanni Franzese, Jeroen Zwanepol, Jens Kober:
Solving Robot Assembly Tasks by Combining Interactive Teaching and Self-Exploration. CoRR abs/2209.11530 (2022) - [i16]Yurui Du, Flavia Sofia Acerbo, Jens Kober, Tong Duy Son:
Learning from Demonstrations of Critical Driving Behaviours Using Driver's Risk Field. CoRR abs/2210.01747 (2022) - [i15]Giovanni Franzese, Leandro de Souza Rosa, Tim Verburg, Luka Peternel, Jens Kober:
Interactive Imitation Learning of Bimanual Movement Primitives. CoRR abs/2210.16220 (2022) - [i14]Carlos Celemin, Rodrigo Pérez-Dattari, Eugenio Chisari, Giovanni Franzese, Leandro de Souza Rosa, Ravi Prakash, Zlatan Ajanovic, Marta Ferraz, Abhinav Valada, Jens Kober:
Interactive Imitation Learning in Robotics: A Survey. CoRR abs/2211.00600 (2022) - [i13]Julian Frederik Schumann, Jens Kober, Arkady Zgonnikov:
Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios. CoRR abs/2211.05455 (2022) - [i12]Jelle Luijkx, Zlatan Ajanovic, Laura Ferranti, Jens Kober:
PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning. CoRR abs/2211.08304 (2022) - 2021
- [j21]Osama Mazhar, Robert Babuska, Jens Kober:
GEM: Glare or Gloom, I Can Still See You - End-to-End Multi-Modal Object Detection. IEEE Robotics Autom. Lett. 6(4): 6321-6328 (2021) - [c46]Antonin Raffin, Jens Kober, Freek Stulp:
Smooth Exploration for Robotic Reinforcement Learning. CoRL 2021: 1634-1644 - [c45]Carlos Celemin, Jens Kober:
Uncertainties based queries for Interactive policy learning with evaluations and corrections. ICMI Companion 2021: 192-193 - [c44]Peter Valletta, Rodrigo Pérez-Dattari, Jens Kober:
Imitation Learning with Inconsistent Demonstrations through Uncertainty-based Data Manipulation. ICRA 2021: 3655-3661 - [c43]Bas van der Heijden, Laura Ferranti, Jens Kober, Robert Babuska:
DeepKoCo: Efficient latent planning with a task-relevant Koopman representation. IROS 2021: 183-189 - [c42]Giovanni Franzese, Anna Mészáros, Luka Peternel, Jens Kober:
ILoSA: Interactive Learning of Stiffness and Attractors. IROS 2021: 7778-7785 - [c41]Bart Bootsma, Giovanni Franzese, Jens Kober:
Interactive Learning of Sensor Policy Fusion. RO-MAN 2021: 665-670 - [i11]Osama Mazhar, Jens Kober:
Random Shadows and Highlights: A new data augmentation method for extreme lighting conditions. CoRR abs/2101.05361 (2021) - [i10]Osama Mazhar, Jens Kober, Robert Babuska:
GEM: Glare or Gloom, I Can Still See You - End-to-End Multimodal Object Detector. CoRR abs/2102.12319 (2021) - [i9]Giovanni Franzese, Anna Mészáros, Luka Peternel, Jens Kober:
ILoSA: Interactive Learning of Stiffness and Attractors. CoRR abs/2103.03099 (2021) - [i8]Anna Mészáros, Giovanni Franzese, Jens Kober:
Teaching Robots to Grasp Like Humans: An Interactive Approach. CoRR abs/2110.04534 (2021) - 2020
- [j20]Fabio Bonsignorio, David Hsu, Matthew Johnson-Roberson, Jens Kober:
Deep Learning and Machine Learning in Robotics [From the Guest Editors]. IEEE Robotics Autom. Mag. 27(2): 20-21 (2020) - [j19]Rodrigo Pérez-Dattari, Carlos Celemin, Giovanni Franzese, Javier Ruiz-del-Solar, Jens Kober:
Interactive Learning of Temporal Features for Control: Shaping Policies and State Representations From Human Feedback. IEEE Robotics Autom. Mag. 27(2): 46-54 (2020) - [j18]Simon Manschitz, Michael Gienger, Jens Kober, Jan Peters:
Learning Sequential Force Interaction Skills. Robotics 9(2): 45 (2020) - [c40]Snehal Jauhri, Carlos Celemin, Jens Kober:
Interactive Imitation Learning in State-Space. CoRL 2020: 682-692 - [c39]Giovanni Franzese, Carlos Celemin, Jens Kober:
Learning Interactively to Resolve Ambiguity in Reference Frame Selection. CoRL 2020: 1298-1311 - [c38]Tom C. T. van Riet, Willem M. de Graaf, Reinier van Antwerpen, Jan van Frankenhuyzen, Jan de Lange, Jens Kober:
Robot Technology in Analyzing Tooth Removal - a Proof of Concept. EMBC 2020: 4721-4727 - [c37]Linda F. van der Spaa, Michael Gienger, Tamas Bates, Jens Kober:
Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks. ICRA 2020: 1799-1805 - [c36]Padmaja Kulkarni, Jens Kober, Robert Babuska:
Tactile-Based Self-supervised Pose Estimation for Robust Grasping. ISER 2020: 277-284 - [e1]Jens Kober, Fabio Ramos, Claire J. Tomlin:
4th Conference on Robot Learning, CoRL 2020, 16-18 November 2020, Virtual Event / Cambridge, MA, USA. Proceedings of Machine Learning Research 155, PMLR 2020 [contents] - [i7]Snehal Jauhri, Carlos Celemin, Jens Kober:
Interactive Imitation Learning in State-Space. CoRR abs/2008.00524 (2020) - [i6]Bas van der Heijden, Laura Ferranti, Jens Kober, Robert Babuska:
DeepKoCo: Efficient latent planning with an invariant Koopman representation. CoRR abs/2011.12690 (2020)
2010 – 2019
- 2019
- [j17]Carlos Celemin, Javier Ruiz-del-Solar, Jens Kober:
A fast hybrid reinforcement learning framework with human corrective feedback. Auton. Robots 43(5): 1173-1186 (2019) - [j16]Yudha P. Pane, Subramanya Nageshrao, Jens Kober, Robert Babuska:
Reinforcement learning based compensation methods for robot manipulators. Eng. Appl. Artif. Intell. 78: 236-247 (2019) - [j15]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) - [c35]Carlos Celemin, Jens Kober:
Simultaneous Learning of Objective Function and Policy from Interactive Teaching with Corrective Feedback. AIM 2019: 726-732 - [c34]Nikolaos Moustakis, Sebastiaan Paul Mulders, Jens Kober, Jan-Willem van Wingerden:
A Practical Bayesian Optimization Approach for the Optimal Estimation of the Rotor Effective Wind Speed. ACC 2019: 4179-4185 - [c33]Jan Scholten, Daan Wout, Carlos Celemin, Jens Kober:
Deep Reinforcement Learning with Feedback-based Exploration. CDC 2019: 803-808 - [c32]Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar, Jens Kober:
Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach. ICRA 2019: 7611-7617 - [i5]Daan Wout, Jan Scholten, Carlos Celemin, Jens Kober:
Learning Gaussian Policies from Corrective Human Feedback. CoRR abs/1903.05216 (2019) - [i4]Jan Scholten, Daan Wout, Carlos Celemin, Jens Kober:
Deep Reinforcement Learning with Feedback-based Exploration. CoRR abs/1903.06151 (2019) - [i3]Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar, Jens Kober:
Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach. CoRR abs/1908.05256 (2019) - 2018
- [j14]Lucian Busoniu, Tim de Bruin, Domagoj Tolic, Jens Kober, Ivana Palunko:
Reinforcement learning for control: Performance, stability, and deep approximators. Annu. Rev. Control. 46: 8-28 (2018) - [j13]Tim de Bruin, Jens Kober, Karl Tuyls, Robert Babuska:
Experience Selection in Deep Reinforcement Learning for Control. J. Mach. Learn. Res. 19: 9:1-9:56 (2018) - [j12]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) - [j11]Tim de Bruin, Jens Kober, Karl Tuyls, Robert Babuska:
Integrating State Representation Learning Into Deep Reinforcement Learning. IEEE Robotics Autom. Lett. 3(2): 1394-1401 (2018) - [c31]Michael Gienger, Dirk Ruiken, Tamas Bates, Mohamed Regaieg, Michael MeiBner, Jens Kober, Philipp Seiwald, Arne-Christoph Hildebrandt:
Human-Robot Cooperative Object Manipulation with Contact Changes. IROS 2018: 1354-1360 - [c30]Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar, Jens Kober:
Interactive Learning with Corrective Feedback for Policies Based on Deep Neural Networks. ISER 2018: 353-363 - [c29]Tamas Bates, Jens Kober, Michael Gienger:
Head-tracked off-axis perspective projection improves gaze readability of 3D virtual avatars. SIGGRAPH Asia Technical Briefs 2018: 29:1-29:4 - [i2]Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar, Jens Kober:
Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks. CoRR abs/1810.00466 (2018) - 2016
- [c28]Jelle Munk, Jens Kober, Robert Babuska:
Learning state representation for deep actor-critic control. CDC 2016: 4667-4673 - [c27]Simon Manschitz, Michael Gienger, Jens Kober, Jan Peters:
Probabilistic decomposition of sequential force interaction tasks into Movement Primitives. IROS 2016: 3920-3927 - [c26]Tim de Bruin, Jens Kober, Karl Tuyls, Robert Babuska:
Improved deep reinforcement learning for robotics through distribution-based experience retention. IROS 2016: 3947-3952 - [p4]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 - 2015
- [j10]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) - [c25]Jens Kober, Michael Gienger, Jochen J. Steil:
Learning movement primitives for force interaction tasks. ICRA 2015: 3192-3199 - [c24]Simon Manschitz, Jens Kober, Michael Gienger, Jan Peters:
Probabilistic progress prediction and sequencing of concurrent movement primitives. IROS 2015: 449-455 - 2014
- [b2]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 - [j9]Jens Kober:
Learning motor skills: from algorithms to robot experiments. it Inf. Technol. 56(3): 141-146 (2014) - [c23]Simon Manschitz, Jens Kober, Michael Gienger, Jan Peters:
Learning to sequence movement primitives from demonstrations. IROS 2014: 4414-4421 - 2013
- [j8]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) - [j7]Jens Kober, J. Andrew Bagnell, Jan Peters:
Reinforcement learning in robotics: A survey. Int. J. Robotics Res. 32(11): 1238-1274 (2013) - [c22]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
- [b1]Jens Kober:
Learning motor skills: from algorithms to robot experiments (Lernen motorischer Fähigkeiten). Darmstadt University of Technology, 2012, pp. 1-133 - [j6]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) - [c21]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 - [c20]Jan Peters, Katharina Mülling, Jens Kober, Duy Nguyen-Tuong, Oliver Krömer:
Robot Skill Learning. ECAI 2012: 40-45 - [c19]Jens Kober, Matthew Glisson, Michael N. Mistry:
Playing catch and juggling with a humanoid robot. Humanoids 2012: 875-881 - [c18]Jens Kober, Katharina Mülling, Jan Peters:
Learning throwing and catching skills. IROS 2012: 5167-5168 - [p3]Jens Kober, Jan Peters:
Reinforcement Learning in Robotics: A Survey. Reinforcement Learning 2012: 579-610 - [p2]Jens Kober:
Lernen Motorischer Fähigkeiten: Von Algorithmen zu Roboter-Experimenten. Ausgezeichnete Informatikdissertationen 2012: 181-190 - [i1]Jens Kober, Jan Peters:
Learning Prioritized Control of Motor Primitives. CoRR abs/1209.0488 (2012) - 2011
- [j5]Katharina Mülling, Jens Kober, Jan Peters:
A biomimetic approach to robot table tennis. Adapt. Behav. 19(5): 359-376 (2011) - [j4]Jens Kober, Jan Peters:
Policy search for motor primitives in robotics. Mach. Learn. 84(1-2): 171-203 (2011) - [c17]Jens Kober, Erhan Öztop, Jan Peters:
Reinforcement Learning to Adjust Robot Movements to New Situations. IJCAI 2011: 2650-2655 - [c16]Jens Kober, Jan Peters:
Learning elementary movements jointly with a higher level task. IROS 2011: 338-343 - [c15]Abdeslam Boularias, Jens Kober, Jan Peters:
Relative Entropy Inverse Reinforcement Learning. AISTATS 2011: 182-189 - 2010
- [j3]Jan Peters, Jens Kober, Stefan Schaal:
Algorithmen zum Automatischen Erlernen von Motorfähigkeiten (Policy Learning Algorithms for Motor Skills). Autom. 58(12): 688-694 (2010) - [j2]Jens Kober, Jan Peters:
Imitation and Reinforcement Learning. IEEE Robotics Autom. Mag. 17(2): 55-62 (2010) - [c14]Katharina Mülling, Jens Kober, Jan Peters:
Learning table tennis with a Mixture of Motor Primitives. Humanoids 2010: 411-416 - [c13]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 - [c12]Katharina Mülling, Jens Kober, Jan Peters:
A biomimetic approach to robot table tennis. IROS 2010: 1921-1926 - [c11]Jan Peters, Katharina Mülling, Jens Kober:
Experiments with Motor Primitives in Table Tennis. ISER 2010: 347-359 - [c10]Jens Kober, Erhan Öztop, Jan Peters:
Reinforcement Learning to adjust Robot Movements to New Situations. Robotics: Science and Systems 2010 - [c9]Katharina Mülling, Jens Kober, Jan Peters:
Simulating Human Table Tennis with a Biomimetic Robot Setup. SAB 2010: 273-282 - [p1]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
2000 – 2009
- 2009
- [j1]Jens Kober, Jan Peters:
Policy Search for Motor Primitives. Künstliche Intell. 23(3): 38-40 (2009) - [c8]Jan Peters, Jens Kober:
Using reward-weighted imitation for robot Reinforcement Learning. ADPRL 2009: 226-232 - [c7]Jens Kober, Jan Peters:
Learning New Basic Movements for Robotics. AMS 2009: 105-112 - [c6]Jens Kober, Jan Peters:
Learning motor primitives for robotics. ICRA 2009: 2112-2118 - [c5]Jan Peters, Katharina Mülling, Jens Kober, Duy Nguyen-Tuong, Oliver Krömer:
Towards Motor Skill Learning for Robotics. ISRR 2009: 469-482 - 2008
- [c4]Jan Peters, Jens Kober, Duy Nguyen-Tuong:
Policy Learning - A Unified Perspective with Applications in Robotics. EWRL 2008: 220-228 - [c3]Jens Kober, Betty J. Mohler, Jan Peters:
Learning perceptual coupling for motor primitives. IROS 2008: 834-839 - [c2]Silvia Chiappa, Jens Kober, Jan Peters:
Using Bayesian Dynamical Systems for Motion Template Libraries. NIPS 2008: 297-304 - [c1]Jens Kober, Jan Peters:
Policy Search for Motor Primitives in Robotics. NIPS 2008: 849-856
Coauthor Index