mlsanityData to 7 Oct 2026

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

arXiv 2205.13771 ↗First posted 27 May 2022cs.CLcited by 14

Not on the map. Why?

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 28 of its 59 references that are in the map. First posted on arXiv 27 May 2022.

Builds on

28 of 59 references.

References · 28

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  5. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  6. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  7. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 774
  8. 2020Recipes for building an open-domain chatbotcited by 593
  9. 2020Towards a Human-like Open-Domain Chatbotcited by 467
  10. 2016How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generationcited by 356
  11. 2019The Second Conversational Intelligence Challenge (ConvAI2)cited by 178
  12. 2018Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Informationcited by 110
16 more
  1. 2020Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learningcited by 84
  2. 2017Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learningcited by 77
  3. 2021Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questionscited by 63
  4. 2020ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)cited by 55
  5. 2020Imitating Interactive Intelligencecited by 55
  6. 2022Improving Intrinsic Exploration with Language Abstractionscited by 49
  7. 2020Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedbackcited by 44
  8. 2019Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Studycited by 40
  9. 2020Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learningcited by 32
  10. 2019Hierarchical Decision Making by Generating and Following Natural Language Instructionscited by 30
  11. 2017Iterative Policy Learning in End-to-End Trainable Task-Oriented Neural Dialog Modelscited by 26
  12. 2022MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learnedcited by 26
  13. 2022Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021cited by 23
  14. 2018Adversarial Learning of Task-Oriented Neural Dialog Modelscited by 17
  15. 2021NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environmentcited by 15
  16. 2019HIGhER : Improving instruction following with Hindsight Generation for Experience Replaycited by 13

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 7; 2024: 4; 2025: 1.

14 papers.

Sort:

By area:

  1. 2024Large Language Model-based Human-Agent Collaboration for Complex Task Solvingcited by 38
  2. 2024Asking the Right Question at the Right Time: Human and Model Uncertainty Guidance to Ask Clarification Questionscited by 32
  3. 2023Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedbackcited by 31
  4. 2023Interactive Natural Language Processingcited by 31
  5. 2023Transforming Human-Centered AI Collaboration: Redefining Embodied Agents Capabilities through Interactive Grounded Language Instructionscited by 13
  6. 2023Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an Analysis of the CoDraw Datasetcited by 11
  7. 2022Collecting Interactive Multi-modal Datasets for Grounded Language Understandingcited by 10
  8. 2022Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language Instructionscited by 9
  9. 2023BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Taskscited by 8
  10. 2025AmbiK: Dataset of Ambiguous Tasks in Kitchen Environmentcited by 8
  11. 2023When and What to Ask Through World States and Text Instructions: IGLU NLP Challenge Solutioncited by 5
  12. 2024Taking Action Towards Graceful Interaction: The Effects of Performing Actions on Modelling Policies for Instruction Clarification Requestscited by 4
2 more
  1. 2023CB2: Collaborative Natural Language Interaction Research Platformcited by 1
  2. 2024Instruction Following with Goal-Conditioned Reinforcement Learning in Virtual Environmentscited by 0