Research
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.
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Segmentation
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Weakly-supervised segmentation of referring expressions
Robin Strudel,
Ivan Laptev,
Cordelia Schmid
arXiV, 2022
arXiv
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bibtex
Learning segmentation from referring expressions, without pixel-level supervision.
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Segmenter: Transformer for Semantic Segmentation
Robin Strudel*,
Ricardo Garcia*,
Ivan Laptev,
Cordelia Schmid
ICCV, 2021
arXiv
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code
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bibtex
Semantic segmentation as a sequence-to-sequence mapping with Vision Transformers.
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Robotics
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Assembly Planning from Observations under Physical Constraints
Thomas Chabal,
Robin Strudel,
Etienne Arlaud,
Jean Ponce,
Cordelia Schmid
arXiv, 2022
arXiv
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bibtex
Assembling structures from a single photograph.
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Learning Obstacle Representations for Neural Motion Planning
Robin Strudel,
Ricardo Garcia,
Justin Carpentier,
Jean-Paul Laumond,
Ivan Laptev,
Cordelia Schmid
CoRL, 2020
arXiv
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project
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code
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bibtex
Visually guided motion planning in unstructured and dynamically changing environments.
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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
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project
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code
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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.
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Learning to Augment Synthetic Images for Sim2Real Policy Transfer
Alexander Pashevich*,
Robin Strudel*,
Igor Kalevatykh,
Ivan Laptev,
Cordelia Schmid
IROS, 2019
arXiv
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project
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code
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bibtex
Learning sim-to-real data augmentation automatically with MCTS and then
transferring policies learned in simulation to a real robot.
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