Robin Strudel

I am a Research Scientist at DeepMind in Paris, where I work on generative models. I obtained my PhD from INRIA and DI ENS, where I was part of the Willow team, and developed learning methods to perform visually-guided tasks on a robot with my advisors Ivan Laptev and Cordelia Schmid.

Before my PhD, I earned a master's degree in mathematics, machine learning and computer vision (MVA) from ENS Paris-Saclay and in probability from ENS Lyon. I also had the opportunity to work as visiting student researcher in the UC Berkeley Department of Statistics for a year under the supervision of Steven N. Evans and as an intern in the Oxford Department of Statistics with Julien Berestycki.

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Self-conditioned Embedding Diffusion for Text Generation
Robin Strudel, Corentin Tallec, Florent Altché, Yilun Du, Yaroslav Ganin, Arthur Mensch, Will Grathwohl, Nikolay Savinov, Sander Dieleman, Laurent Sifre, Rémi Leblond
Preprint, 2022
arXiv / bibtex

A general-purpose and flexible language diffusion model.

Assembly Planning from Observations under Physical Constraints
Thomas Chabal, Robin Strudel, Etienne Arlaud, Jean Ponce, Cordelia Schmid
IROS, 2022
arXiv / bibtex

Assembling structures from a single photograph.

Weakly-supervised segmentation of referring expressions
Robin Strudel, Ivan Laptev, Cordelia Schmid
Preprint, 2022
arXiv / bibtex

Learning segmentation from referring expressions, without pixel-level supervision.

Segmenter: Transformer for Semantic Segmentation
Robin Strudel*, Ricardo Garcia*, Ivan Laptev, Cordelia Schmid
ICCV, 2021
arXiv / code / bibtex

Semantic segmentation as a sequence-to-sequence mapping with Vision Transformers.

Learning Obstacle Representations for Neural Motion Planning
Robin Strudel, Ricardo Garcia, Justin Carpentier, Jean-Paul Laumond, Ivan Laptev, Cordelia Schmid
CoRL, 2020
arXiv / project / code / bibtex

Visually guided motion planning in unstructured and dynamically changing environments.

Learning to combine primitive skills: A step towards versatile robotic manipulation
Robin Strudel*, Alexander Pashevich*, Igor Kalevatykh, Ivan Laptev, Josef Sivic, Cordelia Schmid
ICRA, 2020
arXiv / project / code / bibtex

Learning to perform manipulation tasks with a hierarchical approach. A vocabulary of simple skills is learned from demonstrations then combined with a planning policy to perform more complex tasks.

Learning to Augment Synthetic Images for Sim2Real Policy Transfer
Alexander Pashevich*, Robin Strudel*, Igor Kalevatykh, Ivan Laptev, Cordelia Schmid
IROS, 2019
arXiv / project / code / bibtex

Learning sim-to-real data augmentation automatically with MCTS and then transferring policies learned in simulation to a real robot.

Design and source code from Jon Barron's website.