Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021

Kiseleva, Julia; Li, Ziming; Aliannejadi, Mohammad; Mohanty, Shrestha; Hoeve, Maartje ter; Burtsev, Mikhail; Skrynnik, Alexey; Zholus, Artem; Panov, Aleksandr I.; Srinet, Kavya; Szlam, Arthur; Sun, Yueqiang; Hofmann, Marc-Alexandre Côté Katja; Awadallah, Ahmed Hassan; Abdrazakov, Linar; Churin, Igor; Manggala, Putra; Naszadi, Kata; Meer, M. van der; Kim, Tae-Woon

doi:10.48550/arxiv.2205.02388

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“…Last year (@NeurIPS2021), we organized the first edition of the IGLU competition to tackle the task of grounded language understanding and interactive learning and bring together the NLU/G and RL research communities Kiseleva et al [ , 2022. In light of the competition results, it is clear the task is far from being solved and requires more collective effort to explore promising directions.…”

Section: Noveltymentioning

confidence: 99%

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

Kiseleva¹,

Skrynnik²,

Zholus³

et al. 2022

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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 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 important challenges in AI. Another important aspect of the challenge is the dedication to perform a human-inthe-loop evaluation as a final evaluation for the agents developed by contestants.

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Section: Noveltymentioning

confidence: 99%