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

TL;DR

The paper introduces IGLU, a competition at NeurIPS 2022 focused on developing interactive embodied agents that understand and follow grounded natural language instructions in collaborative environments, aiming to advance AI in natural language understanding and reinforcement learning.

Contribution

It presents a new research challenge and benchmark for creating agents capable of learning from natural language instructions through interactive, collaborative tasks, integrating NLU and RL.

Findings

Split into sub-tasks to make the challenge feasible

Encourages collaboration between NLU and RL communities

Includes human-in-the-loop evaluation for final assessment

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.