Alexander Pashevich

I am a PhD student at Inria working with Cordelia Schmid and Ivan Laptev. Recently, I did an internship at Google Research where I worked with Chen Sun. I did my masters at the Grenoble Institute of Technology and bachelors at the Moscow Institute of Physics and Technology.

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My research interests include robotics, reinforcement and imitation learning, simulation-to-reality transfer, natural language processing, and vision-and-language navigation. Please take a look at my CV and a list of publications below.

fast-texture Episodic Transformer for Vision-and-Language Navigation
Alexander Pashevich, Cordelia Schmid, Chen Sun
bibtex / code

An attention-based architecture for vision-and-language navigation.

fast-texture Learning visual policies for building 3D shape categories
Alexander Pashevich*, Igor Kalevatykh*, Ivan Laptev, Cordelia Schmid
IROS, 2020
bibtex / website / video

An approach learning to build shapes by disassembly.

fast-texture 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
bibtex / code / website / video

A reinforcement learning approach to task planning that learns to combine primitive skills.

fast-texture Learning to Augment Synthetic Images for Sim2Real Policy Transfer
Alexander Pashevich*, Robin Strudel*, Igor Kalevatykh, Ivan Laptev, Cordelia Schmid
IROS, 2019
bibtex / code / website / video

An approach for transferring policies learned in simulation to the real world.

fast-texture Modulated Policy Hierarchies
Alexander Pashevich, Danijar Hafner, James Davidson, Rahul Sukthankar, Cordelia Schmid
NeurIPS Deep RL Workshop, 2018
bibtex / poster

A hierarchical reinforcement learning approach for learning from sparse rewards.

fast-texture Plane-extraction from depth-data using a Gaussian mixture regression model
Richard Marriott, Alexander Pashevich, Radu Horaud
Pattern Recognition Letters, 2018

An algorithm for unsupervised extraction of piecewise planar models from depth data.

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