Julen Urain
Papers
13
Total Citations
282
H-Index
6
About
Julen Urain is a leading researcher at the intersection of robotics, machine learning, and motion generation, with a focus on enabling robots to perform complex, multi-objective manipulation tasks. His work centers on learning-based approaches for motion optimization, reactive control, and human-robot interaction. Urain’s major contributions include the development of SE(3)-DiffusionFields, a diffusion-based framework that jointly optimizes grasp and motion, cited 89 times, and his pioneering use of deep reinforcement learning for pick-and-place logistics with mobile manipulators (81 citations). He has advanced the field by introducing implicit priors for motion optimization and stable vector fields on Lie groups, enabling smooth, reactive, and safe robot motions. His research on human intention detection for physical human-robot interaction during walking (41 citations) and composable energy policies for reactive motion generation further highlights his impact. Urain’s work has been recognized for its reproducibility, notably through his role in building a shared robot cluster for dexterous manipulation research. With over 270 cumulative citations, his contributions are shaping the future of autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
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- 4Learning Implicit Priors for Motion Optimization21 citations · 2022
- 5Learning Stable Vector Fields on Lie Groups20 citations · 2022
- 6Hierarchical Policy Blending as Inference for Reactive Robot Control8 citations · 2023
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- 10A Robot Cluster for Reproducible Research in Dexterous Manipulation3 citations · 2021