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

arXiv: https://arxiv.org/abs/2205.13771 · HTML: https://mlsanity.com/paper/2205.13771/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 59 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016)
- [The Second Conversational Intelligence Challenge (ConvAI2)](https://mlsanity.com/paper/1902.00098/) (2019)
- [Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information](https://mlsanity.com/paper/1805.04655/) (2018)
- [Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning](https://mlsanity.com/paper/2006.11751/) (2020)
- [Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning](https://mlsanity.com/paper/1712.07294/) (2017)
- [Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions](https://mlsanity.com/paper/2109.05794/) (2021)
- [ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)](https://mlsanity.com/paper/2009.11352/) (2020)
- [Imitating Interactive Intelligence](https://mlsanity.com/paper/2012.05672/) (2020)
- [Improving Intrinsic Exploration with Language Abstractions](https://mlsanity.com/paper/2202.08938/) (2022)
- [Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback](https://mlsanity.com/paper/2005.02539/) (2020)
- [Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study](https://mlsanity.com/paper/1910.05389/) (2019)
- [Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning](https://mlsanity.com/paper/2011.00517/) (2020)
- [Hierarchical Decision Making by Generating and Following Natural Language Instructions](https://mlsanity.com/paper/1906.00744/) (2019)
- [Iterative Policy Learning in End-to-End Trainable Task-Oriented Neural Dialog Models](https://mlsanity.com/paper/1709.06136/) (2017)
- [MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned](https://mlsanity.com/paper/2202.10583/) (2022)
- [Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021](https://mlsanity.com/paper/2205.02388/) (2022)
- [Adversarial Learning of Task-Oriented Neural Dialog Models](https://mlsanity.com/paper/1805.11762/) (2018)
- [NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment](https://mlsanity.com/paper/2110.06536/) (2021)
- [HIGhER : Improving instruction following with Hindsight Generation for Experience Replay](https://mlsanity.com/paper/1910.09451/) (2019)

## Built on by (most cited first, 14 of 14)

- [Large Language Model-based Human-Agent Collaboration for Complex Task Solving](https://mlsanity.com/paper/2402.12914/) (2024)
- [Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback](https://mlsanity.com/paper/2304.10750/) (2023)
- [Asking the Right Question at the Right Time: Human and Model Uncertainty Guidance to Ask Clarification Questions](https://mlsanity.com/paper/2402.06509/) (2024)
- [Interactive Natural Language Processing](https://mlsanity.com/paper/2305.13246/) (2023)
- [Transforming Human-Centered AI Collaboration: Redefining Embodied Agents Capabilities through Interactive Grounded Language Instructions](https://mlsanity.com/paper/2305.10783/) (2023)
- [Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an Analysis of the CoDraw Dataset](https://mlsanity.com/paper/2302.14406/) (2023)
- [Collecting Interactive Multi-modal Datasets for Grounded Language Understanding](https://mlsanity.com/paper/2211.06552/) (2022)
- [Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language Instructions](https://mlsanity.com/paper/2211.00688/) (2022)
- [AmbiK: Dataset of Ambiguous Tasks in Kitchen Environment](https://mlsanity.com/paper/2506.04089/) (2025)
- [BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks](https://mlsanity.com/paper/2312.02405/) (2023)
- [When and What to Ask Through World States and Text Instructions: IGLU NLP Challenge Solution](https://mlsanity.com/paper/2305.05754/) (2023)
- [Taking Action Towards Graceful Interaction: The Effects of Performing Actions on Modelling Policies for Instruction Clarification Requests](https://mlsanity.com/paper/2401.17039/) (2024)
- [CB2: Collaborative Natural Language Interaction Research Platform](https://mlsanity.com/paper/2303.08127/) (2023)
- [Instruction Following with Goal-Conditioned Reinforcement Learning in Virtual Environments](https://mlsanity.com/paper/2407.09287/) (2024)

## Abstract (arXiv)

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.