Scholar
Robin Strudel
Google Scholar ID: 5d3RVLIAAAAJ
Google DeepMind, Inria, ENS
Citations & Impact
Citations
12,797
H-index
12
i10-index
12
Publications
16
Co-authors
20
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Published several papers, including 'Self-conditioned Embedding Diffusion for Text Generation' (2022), 'Assembly Planning from Observations under Physical Constraints' (2022), 'Weakly-supervised Segmentation of Referring Expressions' (2022), 'Segmenter: Transformer for Semantic Segmentation' (2021), 'Learning Obstacle Representations for Neural Motion Planning' (2020), 'Learning to Combine Primitive Skills: A Step Towards Versatile Robotic Manipulation' (2020), 'Learning to Augment Synthetic Images for Sim2Real Policy Transfer' (2019).
Research Experience
- Research Scientist at DeepMind, Paris; Involved in multiple research projects including self-conditioned embedding diffusion for text generation, assembly planning from observations under physical constraints, weakly-supervised segmentation of referring expressions, etc.
Education
- PhD from INRIA and DI ENS, Advisors: Ivan Laptev and Cordelia Schmid; Master's degree in mathematics, machine learning, and computer vision (MVA) from ENS Paris-Saclay and in probability from ENS Lyon; Visiting student researcher at the UC Berkeley Department of Statistics for one year under Steven N. Evans; Intern at the Oxford Department of Statistics with Julien Berestycki.
Background
- Research Interests: Generative models. Specialization: Learning methods for performing visually-guided tasks on a robot.
