Julen Urain

Research Scientist at Meta's FAIR

julen.jpg

I am a research scientist at Meta FAIR. Previously, I was a postdoctoral researcher at the Intelligent Autonomous Systems lab (IAS) and the DFKI. I received my PhD in Computer Science with summa cum laude from TU Darmstadt under the supervision of Prof. Jan Peters. Previously, I interned as a researcher in Nvidia’s Seattle Robotics Lab (SRL). My research have received several awards including several best paper awards and I was finalist for the George Giralt PhD award. I was honoured to be selected as an R:SS Pioneer in 2023.

My research interests lie at the intersection of robotics and machine learning. In particular I explore the combination of fields such as deep generative models, motion planning and control, imitation learning, optimization, and reinforcement learning.

If you are interested in similar topics, I am always looking for collaborations or thesis supervision, so please do not hesitate to contact me.

Contact: julen [at] robot-learning [dot] de

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news

Jan 06, 2025 I just joined Meta’s FAIR as a research scientist. I will be working in dexterous manipulation :)
Jul 20, 2024 We won Best Paper Award in Structural Priors as Inductive Biases for Learning Robot Dynamics at RSS 2024 for our work on ActionFlows.
Feb 07, 2024 I have been selected as finalist for the George Girault Ph.D. award!! Europe’s highest honor for a robotics dissertation :heart:
Dec 18, 2023 I succesfully defended my Ph.D with Suma Cum Laude :blush:
Jun 02, 2023 We won Best Paper Award in Geometric Representations Workshop at ICRA 2023 for our work on SE(3) DiffusionFields.

selected publications

  1. se3dif.gif
    SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion
    J. Urain ,  N. Funk ,  G. Chalvatzaki , and 1 more author
    ICRA, 2023
    arXiv
  2. msvf_2022.gif
    Learning Stable Vector Fields on Lie Groups
    J. Urain ,  D. Tateo ,  and  J. Peters
    RA-L / ICRA, 2022
    arXiv
  3. rss_2021.gif
    Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning
    J. Urain ,  A. Li ,  P. Liu , and 2 more authors
    R:SS / IJRR, 2021
    arXiv
  4. ral_2021.gif
    Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation
    N. Funk ,  C. Schaff ,  R. Madan , and 8 more authors
    RA-L, 2021
    arXiv