Julen Urain De Jesus

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Research Interests

Probabilistic Modelling, Stochastic Dynamical Systems, Generative Models, Representation Learning

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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

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).

During his Ph.D., Julen is 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.

Research Interest

Probabilistic Modelling, Stochastic Dynamical Systems, Generative Models, Machine Learning, Robotics, Representation Learning, Inverse Reinforcement Learning, Optimal Control, Trajectory Optimization.

Key References

  1. Urain, J.; Peters, J. (2019). Generalized Multiple Correlation Coefficient as a Similarity Measurement between Trajectories, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   Download Article [PDF]   BibTeX Reference [BibTex]
  2. 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 [PDF]   BibTeX Reference [BibTex]
  3. Urain, J.; Tateo, D.; Ren, T.; Peters, J. (2020). Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems, NeurIPS 2020, 3rd Robot Learning Workshop, pp.7.   Download Article [PDF]   BibTeX Reference [BibTex]

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