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.

翻译：人类智力具有迅速适应新任务和环境的非凡能力。从很小的时代开始,人类获得新的技能,学会如何通过模仿他人的行为或遵循自然语言指令来完成新任务。为了便利这方面的研究,我们建议IGLU:在合作环境中互动基语言理解。竞争的首要目标是解决如何培养互动的、有代表性的代理机构的问题,这些代理机构在合作环境中学会解决一项任务,同时提供有根有据的自然语言指令。理解挑战的复杂性,我们将其分成子任务,使参与者能够参与。这一研究挑战自然地与NeurIPS社区密切相关,但不限于与自然语言理解和生成(NLU/G)和加强学习(RL)高度相关的两个研究领域。因此,所建议的挑战可以促使两个社区共同解决AI中的关键挑战之一。挑战的另一个关键方面是致力于进行“人与人之间的接触评估”,作为竞争者对代理人的最后评估。