NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment
This competition tackles the challenge of developing AI agents that can adapt to new tasks via human-like instruction following, which is incremental as it builds on existing fields but organizes them into a structured benchmark.
The paper introduces the IGLU competition to address the problem of building interactive agents that learn tasks through grounded natural language instructions in collaborative environments, aiming to bridge Natural Language Understanding/Generation and Reinforcement Learning communities.
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 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. 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-in-the-loop evaluation as a final evaluation for the agents developed by contestants.