Artem Zholus

I am a research intern at EPFL working in VILAB supervised by Prof. Amir Zamir. My current research focuses on learning structure between actions and objects. My ultimate research goal is to build adaptive agents that can act under open-ended goals. For that, I leverage reinforcement learning, computer vision, and natural language processing.

Before, I was a summer intern working at the LIONS lab, EPFL under Prof. Volkan Cevher. I obtained my Masters degree at MIPT studying AI, ML, and Cognitive Sciences and working at the CDS lab under Prof. Aleksandr Panov on task generalization in model-based reinforcement learning. I received my BSc degree from ITMO University majoring in Computer Science and Applied Mathematics.

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Publications

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IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents


Artem Zholus, Alexey Skrynnik, Shrestha Mohanty, Zoya Volovikova, Julia Kiseleva, Artur Szlam, Marc-Alexandre Coté, Aleksandr I. Panov
Embodied AI workshop @ CVPR, 2022
arxiv / code / slides /

A lightweight reinforcement learning environment for building embodied agents with language context tasked to build 3D structures in Minecraft-like world.

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IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022


Julia Kiseleva*, Alexey Skrynni*, Artem Zholus*, Shrestha Mohanty*, Negar Arabzadeh*, Marc-Alexandre Côté*, Mohammad Aliannejadi, Milagro Teruel, Ziming Li, Mikhail Burtsev, Maartje ter Hoeve, Zoya Volovikova, Aleksandr Panov, Yuxuan Sun, Kavya Srinet, Arthur Szlam, Ahmed Awadallah
NeurIPS, Competition Track, 2022
website / arxiv / code /

AI competition where goal is to follow a language instruction with context being embodied in 3D blocks world (RL track) and to ask a clarifying question in the case of ambiguity (NLP track).

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Factorized World Models for Learning Causal Relationships


Artem Zholus, Yaroslav Ivchenkov, and Aleksandr Panov
OSC workshop, ICLR, 2022
arxiv / code /

An RL agent that can generalize behaviour on unseen tasks, which is done by learning a structured world model.


Design and source code from Jon Barron's website