Julen Urain De Jesus Team
Julen Urain
Quick Info
Research Interests
Probabilistic Modelling, Stochastic Dynamical Systems, Generative Models, Representation Learning
More Information
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Contact Information
Mail.
Julen Urain De Jesus
TU Darmstadt, FG IAS,
Hochschulstr. 10, 64289 Darmstadt
Office.
Room E225, Building S2|02
+49-6151-16-20073
julen.urain_de_jesus@tu-darmstadt.de
Julen joined the Intelligent Autonomous Systems Group at TU Darmstadt as a Ph.D. researcher in January 2019. Julen received his MS in Automatic Control and Robotics From UPC (Barcelona) in November 2017. Julen has gained a lot of knowledge in machine learning and robotics from different institutions. He developed his master thesis in BioRob lab in EPFL(Laussane) under the supervision of Auke Ijspeert and Jessica Lanini, He did an internship in the first edition of Deep Learning and Robotics Challenge (DLRC) in VW Data:Lab (Munich) and for the last year from December 2017 to December 2018 He has worked as a robotics researcher in IK4-Tekniker (Eibar).
Research Topic
My main research line is in the integration of machine learning algorithms to learn complex manipulation skills. During the Ph.D., I am looking for new ways of representing the environment and policies, following probabilistic models. It is expected that better models for representing them will improve the prediction, classification, and generation of motion applied in several fields from Imitation Learning to Human-Robot Interaction. In my thesis, I study the integration of Generative Modelling methods, Energy Based Models (EBM), Normalizing Flows with Robotics theory (Stability, Riemannian Geometry, Safety) to integrate in a smart way generative modelling methods into robotics and learn complex manipulation tasks.
Research Interest
Machine Learning
Probabilistic Modelling, Generative Models, Energy Based Models, Normalizing Flows, Score Based Models
Robotics and Control
Stochastic Dynamical Systems, Stability, Trajectory Optimization, Model Predictive Control, Reactive Motion Generation
Robot Learning
Inverse Reinforcement Learning, Imitation Learning, cost learning
Key References
Diffusion Models in Robotics
- Urain, J.; Funk, N; Peters, J. ; Chalvatzaki G (2023). SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion, International Conference on Robotics and Automation (ICRA). Download Article BibTeX Reference
- Urain, J.; Le, A.T.; Lambert, A.; Chalvatzaki, G.; Boots, B.; Peters, J. (2022). Learning Implicit Priors for Motion Optimization, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Download Article BibTeX Reference
Skill Composition Models
- Urain, J.; Li, A.; Liu, P.; D'Eramo, C.; Peters, J. (in press). Composable energy policies for reactive motion generation and reinforcement learning, International Journal of Robotics Research (IJRR).
BibTeX Reference
Urain, J.; Li, A.; Liu, P.; D'Eramo, C.; Peters, J. (2021). Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning, Robotics: Science and Systems (RSS). Download Article BibTeX Reference - Urain, J.; Le, A.T.; Lambert, A.; Chalvatzaki, G.; Boots, B.; Peters, J. (2022). Learning Implicit Priors for Motion Optimization, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Download Article BibTeX Reference
Learning Stable dynamic Systems
- Urain, J.; Tateo, D.; Peters, J. (2023). Learning Stable Vector Fields on Lie Groups, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE R-AL Track.
BibTeX Reference
Urain, J.; Tateo, D; Peters, J. (2022). Learning Stable Vector Fields on Lie Groups, Robotics and Automation Letters (RA-L). Download Article BibTeX Reference - Urain, J.; Ginesi, M.; Tateo, D.; Peters, J. (2020). ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows, IEEE/RSJ International Conference on Intelligent Robots and Systems. Download Article BibTeX Reference
Completed Theses and Projects
| Start | Type | In coorperation with | Student(s) | Topic | Document |
|---|---|---|---|---|---|
| 2021 | Master's Thesis | Yifei Wang | Bimanual Control and Learning with Composable Energy Policies | ||
| 2021 | Master's Thesis | Jiawei Huang | Multi-Objective Reactive Motion Planning in Mobile Manipulators | ||
| 2021 | Master's Thesis | Hanyu Sun | Can we improve time-series classification with Inverse Reinforcement Learning? | ||
| 2021 | Master's Thesis | Lanmiao Liu | Detection and Prediction of Human Gestures by Probabilistic Modelling | ||
| 2020 | Master Thesis | Puze Liu | Zhenhui Zhou | Approximated Policy Search in Black-Box Optimization | |
| 2021 | Integrated Project | Joao Carvalho | Jascha Hellwig, Mark Baierl | Active Visual Search with POMDPs | |
| 2021 | Integrated Project | Johannes Weyel | Utilizing 6D Pose-Estimation over ROS | ||
| 2021 | Integrated Project | Puze Liu | Niklas Babendererde, Johannes Weyel | Syntethic Dataset generation for Articulation prediction | |
| 2020 | Integrated Project | Puze Liu | Niklas Babendererde, Jiawei Huang | Benchmarking Multi-Arm Bandit & Black Box optimization(DFO) 4 Grasping | |
| 2020 | Integrated Project | Lanmiao Liu, Hanyu Sun | Can we use Structured Inference Networks for Human Motion Prediction? | ||
| 2020 | Integrated Project | Pengfei Zhao | Towards Semantic Imitation Learning |
