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

• URL: http://arxiv.org/abs/2205.13771v1
• Date: Fri, 27 May 2022 06:12:48 GMT
• Title: IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022
• Authors: Julia Kiseleva and Alexey Skrynnik and Artem Zholus and Shrestha Mohanty and Negar Arabzadeh and Marc-Alexandre C\^ot\'e and Mohammad Aliannejadi and Milagro Teruel and Ziming Li and Mikhail Burtsev and Maartje ter Hoeve and Zoya Volovikova and Aleksandr Panov and Yuxuan Sun and Kavya Srinet and Arthur Szlam and Ahmed Awadallah
• Abstract summary: 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 develop interactive embodied agents. This research challenge is naturally related, but not limited, to two fields of study that are highly relevant to the NeurIPS community.
• Score: 63.07251290802841
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human intelligence has the remarkable ability to adapt to new tasks and environments quickly. 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 develop interactive embodied 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. This research challenge is naturally related, but not limited, to two fields of study that are highly relevant to the NeurIPS community: Natural Language Understanding and Generation (NLU/G) and Reinforcement Learning (RL). Therefore, the suggested challenge can bring two communities together to approach one of the crucial challenges in AI. Another critical aspect of the challenge is the dedication to perform a human-in-the-loop evaluation as a final evaluation for the agents developed by contestants.

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