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

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arxiv 2205.13771 v1 pith:UFRSEBBQ submitted 2022-05-27 cs.CL

classification cs.CL
keywords languagechallengeunderstandingcollaborativeenvironmentgroundedinteractivenatural
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. AmbiK: Dataset of Ambiguous Tasks in Kitchen Environment

    cs.LG 2025-06 conditional novelty 6.0 of 10

    AmbiK is a human-validated, text-only benchmark of 1000 ambiguous kitchen tasks paired with unambiguous counterparts, on which current ambiguity detection methods perform poorly.

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