mlsanityData to 14 Oct 2026

Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021

arXiv 2205.02388 ↗First posted 5 May 2022cs.CL cs.AIcited by 23

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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.

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

37 of 80 references.

References · 37

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25 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 5; 2024: 8; 2025: 4; 2026: 2.

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  1. 2023HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real Worldcited by 166
  2. 2023Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedbackcited by 32
  3. 2023Creative Agents: Empowering Agents with Imagination for Creative Taskscited by 24
  4. 2024S-Agents: Self-organizing Agents in Open-ended Environmentscited by 22
  5. 2024Assessing and Verifying Task Utility in LLM-Powered Applicationscited by 16
  6. 2022IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022cited by 14
  7. 2023Transforming Human-Centered AI Collaboration: Redefining Embodied Agents Capabilities through Interactive Grounded Language Instructionscited by 13
  8. 2022Collecting Interactive Multi-modal Datasets for Grounded Language Understandingcited by 10
  9. 2025AssistanceZero: Scalably Solving Assistance Gamescited by 10
  10. 2022IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agentscited by 9
  11. 2022Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language Instructionscited by 9
  12. 2024Large Language Models as Minecraft Agentscited by 7
11 more
  1. 2024Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applicationscited by 5
  2. 2025Referential ambiguity and clarification requests: comparing human and LLM behaviourcited by 5
  3. 2024A LLM Benchmark based on the Minecraft Builder Dialog Agent Taskcited by 4
  4. 2024Nebula: A discourse aware Minecraft Buildercited by 3
  5. 2023Interactive Text Generationcited by 2
  6. 2025MDC-R: The Minecraft Dialogue Corpus with Referencecited by 2
  7. 2024IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agentscited by 1
  8. 2024Instruction Following with Goal-Conditioned Reinforcement Learning in Virtual Environmentscited by 0
  9. 2025Making Dialogue Grounding Data Rich: A Three-Tier Data Synthesis Framework for Generalized Referring Expression Comprehensioncited by 0
  10. 20262.5-D Decomposition for LLM-Based Spatial Constructioncited by 0
  11. 2026Advancing DialNav through Automatic Embodied Dialog Augmentationcited by 0