Robin Strudel

I am a fourth year PhD student at INRIA and DI ENS, in the Willow team, where I work on computer vision and robotics with Ivan Laptev and Cordelia Schmid. I am currently interning at DeepMind and I will be graduating in Fall 2022.

Before my PhD, I graduated from ENS Paris-Saclay with a Masters degree in mathematics, machine learning and computer vision (MVA) and from ENS Lyon with a Masters degree in probability. I was a visiting student researcher in the UC Berkeley Department of Statistics for a year under the supervision of Steven N. Evans and an intern in the Oxford Department of Statistics with Julien Berestycki.

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I am interested in computer vision, machine learning and robotics. I have been working on scene understanding and learning to perform visually guided manipulation tasks with a robot. My research relates to image segmentation, imitation learning, reinforcement learning and sim-to-real transfer.

Weakly-supervised segmentation of referring expressions
Robin Strudel, Ivan Laptev, Cordelia Schmid
arXiV, 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.

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

Assembling structures from a single photograph.

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.