DATE OF PUBLICATION
Mikhail Burtsev Alexander Panov Alexey Skrynnik Artem Zholus Shrestha Mohanty Julia Kiseleva Ziming Li Mohammad Aliannejadi Maartje ter Hoeve Kavya Srinet Arthur Szlam Yuxuan Sun Marc-Alexandre Cotˆe Katja Hofmann Ahmed Awadallah Linar Abdrazakov Igor Churin Putra Manggala Kata Naszadi Michiel van der Meer Taewoon Kim
Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2022
Natural Language Understanding (NLU), Reinforcement Learning (RL), Grounded Learning, Interactive Learning, Games
Human intelligence has the remarkable ability to quickly adapt to new tasks and environments. Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions. To facilitate research in this direction, we propose IGLU: Interactive Grounded Language Understanding in a Collaborative Environment. The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment. Understanding the complexity of the challenge, we split it into sub-tasks to make it feasible for participants.
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