Alexander Pashevich

I am a Ph.D. specializing in robotics, computer vision, and natural language processing. Over the last four years, I pioneered robot learning research in the team and developed methods to enable robots to learn visually- and language-guided behaviors from data. In the five publications at top-tier conferences, I showed advantages of my work over classical control algorithms. I also developed a method to transfer complex skills learned in simulation to real-world UR5 robots. I am now excited about the next challenge of applying my research knowledge to real-world products.

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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. During the Ph.D. studies, I was adviced by Cordelia Schmid and co-adviced by Ivan Laptev. I worked with Chen Sun during my internship at Google Research.

fast-texture Episodic Transformer for Vision-and-Language Navigation
Alexander Pashevich, Cordelia Schmid, Chen Sun
ICCV, 2021
bibtex / code / website

A multimodal transformer-based architecture for vision-and-language navigation (VLN) improving results on a challenging task by 74%.

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 that combines learning in low-dimensional state space and high-dimensional observation space.

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 learned from demonstrations.

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 real-world robots by exploiting parallel computations.

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 using constrained Gaussian mixture models.

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