# Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021

arXiv: https://arxiv.org/abs/2205.02388 · HTML: https://mlsanity.com/paper/2205.02388/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 80 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 30 of 37)

- [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)
- [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)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Mastering Atari with Discrete World Models](https://mlsanity.com/paper/2010.02193/) (2020)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [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)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [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)
- [Distributed Prioritized Experience Replay](https://mlsanity.com/paper/1803.00933/) (2018)
- [The Second Conversational Intelligence Challenge (ConvAI2)](https://mlsanity.com/paper/1902.00098/) (2019)
- [Learning to Speak and Act in a Fantasy Text Adventure Game](https://mlsanity.com/paper/1903.03094/) (2019)
- [Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information](https://mlsanity.com/paper/1805.04655/) (2018)
- [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)
- [RLlib: Abstractions for Distributed Reinforcement Learning](https://mlsanity.com/paper/1712.09381/) (2017)
- [Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback](https://mlsanity.com/paper/2005.02539/) (2020)
- [Program Synthesis using Natural Language](https://mlsanity.com/paper/1509.00413/) (2015)
- [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)
- [ConvLab: Multi-Domain End-to-End Dialog System Platform](https://mlsanity.com/paper/1904.08637/) (2019)
- [CraftAssist: A Framework for Dialogue-enabled Interactive Agents](https://mlsanity.com/paper/1907.08584/) (2019)
- [Iterative Policy Learning in End-to-End Trainable Task-Oriented Neural Dialog Models](https://mlsanity.com/paper/1709.06136/) (2017)
- [The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors](https://mlsanity.com/paper/1904.10079/) (2019)
- [The Eighth Dialog System Technology Challenge](https://mlsanity.com/paper/1911.06394/) (2019)

## Built on by (most cited first, 23 of 23)

- [HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real World](https://mlsanity.com/paper/2309.17024/) (2023)
- [Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback](https://mlsanity.com/paper/2304.10750/) (2023)
- [Creative Agents: Empowering Agents with Imagination for Creative Tasks](https://mlsanity.com/paper/2312.02519/) (2023)
- [S-Agents: Self-organizing Agents in Open-ended Environments](https://mlsanity.com/paper/2402.04578/) (2024)
- [Assessing and Verifying Task Utility in LLM-Powered Applications](https://mlsanity.com/paper/2405.02178/) (2024)
- [IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022](https://mlsanity.com/paper/2205.13771/) (2022)
- [Transforming Human-Centered AI Collaboration: Redefining Embodied Agents Capabilities through Interactive Grounded Language Instructions](https://mlsanity.com/paper/2305.10783/) (2023)
- [Collecting Interactive Multi-modal Datasets for Grounded Language Understanding](https://mlsanity.com/paper/2211.06552/) (2022)
- [AssistanceZero: Scalably Solving Assistance Games](https://mlsanity.com/paper/2504.07091/) (2025)
- [IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents](https://mlsanity.com/paper/2206.00142/) (2022)
- [Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language Instructions](https://mlsanity.com/paper/2211.00688/) (2022)
- [Large Language Models as Minecraft Agents](https://mlsanity.com/paper/2402.08392/) (2024)
- [Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications](https://mlsanity.com/paper/2402.09015/) (2024)
- [Referential ambiguity and clarification requests: comparing human and LLM behaviour](https://mlsanity.com/paper/2507.10445/) (2025)
- [A LLM Benchmark based on the Minecraft Builder Dialog Agent Task](https://mlsanity.com/paper/2407.12734/) (2024)
- [Nebula: A discourse aware Minecraft Builder](https://mlsanity.com/paper/2406.18164/) (2024)
- [Interactive Text Generation](https://mlsanity.com/paper/2303.00908/) (2023)
- [MDC-R: The Minecraft Dialogue Corpus with Reference](https://mlsanity.com/paper/2506.22062/) (2025)
- [IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents](https://mlsanity.com/paper/2407.08898/) (2024)
- [Instruction Following with Goal-Conditioned Reinforcement Learning in Virtual Environments](https://mlsanity.com/paper/2407.09287/) (2024)
- [Making Dialogue Grounding Data Rich: A Three-Tier Data Synthesis Framework for Generalized Referring Expression Comprehension](https://mlsanity.com/paper/2512.02791/) (2025)
- [2.5-D Decomposition for LLM-Based Spatial Construction](https://mlsanity.com/paper/2605.07066/) (2026)
- [Advancing DialNav through Automatic Embodied Dialog Augmentation](https://mlsanity.com/paper/2606.19948/) (2026)

## Abstract (arXiv)

Human intelligence has the remarkable ability to quickly adapt to new tasks and environments. 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.