# CraftAssist: A Framework for Dialogue-enabled Interactive Agents

arXiv: https://arxiv.org/abs/1907.08584 · HTML: https://mlsanity.com/paper/1907.08584/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2019.

## Built on by (most cited first, 28 of 28)

- [MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge](https://mlsanity.com/paper/2206.08853/) (2022)
- [Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback](https://mlsanity.com/paper/2304.10750/) (2023)
- [The MineRL BASALT Competition on Learning from Human Feedback](https://mlsanity.com/paper/2107.01969/) (2021)
- [Creative Agents: Empowering Agents with Imagination for Creative Tasks](https://mlsanity.com/paper/2312.02519/) (2023)
- [Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021](https://mlsanity.com/paper/2205.02388/) (2022)
- [S-Agents: Self-organizing Agents in Open-ended Environments](https://mlsanity.com/paper/2402.04578/) (2024)
- [A Systematic Survey of Text Worlds as Embodied Natural Language Environments](https://mlsanity.com/paper/2107.04132/) (2021)
- [NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment](https://mlsanity.com/paper/2110.06536/) (2021)
- [Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition](https://mlsanity.com/paper/2303.13512/) (2023)
- [Transforming Human-Centered AI Collaboration: Redefining Embodied Agents Capabilities through Interactive Grounded Language Instructions](https://mlsanity.com/paper/2305.10783/) (2023)
- [AssistanceZero: Scalably Solving Assistance Games](https://mlsanity.com/paper/2504.07091/) (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)
- [The slurk Interaction Server Framework: Better Data for Better Dialog Models](https://mlsanity.com/paper/2202.01155/) (2022)
- [Large Language Models as Minecraft Agents](https://mlsanity.com/paper/2402.08392/) (2024)
- [Retrospective on the 2021 BASALT Competition on Learning from Human Feedback](https://mlsanity.com/paper/2204.07123/) (2022)
- [A LLM Benchmark based on the Minecraft Builder Dialog Agent Task](https://mlsanity.com/paper/2407.12734/) (2024)
- [droidlet: modular, heterogenous, multi-modal agents](https://mlsanity.com/paper/2101.10384/) (2021)
- [CrafText Benchmark: Advancing Instruction Following in Complex Multimodal Open-Ended World](https://mlsanity.com/paper/2505.11962/) (2025)
- [Modular Procedural Generation for Voxel Maps](https://mlsanity.com/paper/2104.08890/) (2021)
- [IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents](https://mlsanity.com/paper/2407.08898/) (2024)
- [Enabling Rapid Shared Human-AI Mental Model Alignment via the After-Action Review](https://mlsanity.com/paper/2503.19607/) (2025)
- [Can Current Agents Close the Discovery-to-Application Gap? A Case Study in Minecraft](https://mlsanity.com/paper/2604.24697/) (2026)
- [Mind Reading at Work: Cooperation without common ground](https://mlsanity.com/paper/2105.01949/) (2021)
- [Using reinforcement learning to design an AI assistantfor a satisfying co-op experience](https://mlsanity.com/paper/2105.03414/) (2021)
- [Neural Abstructions: Abstractions that Support Construction for Grounded Language Learning](https://mlsanity.com/paper/2107.09285/) (2021)
- [Feudal Reinforcement Learning by Reading Manuals](https://mlsanity.com/paper/2110.06477/) (2021)
- [Self-Guided Plan Extraction for Instruction-Following Tasks with Goal-Conditional Reinforcement Learning](https://mlsanity.com/paper/2604.20601/) (2026)
- [MineCEraft: Evaluating Language Models as Construction Engineers in the World of Minecraft](https://mlsanity.com/paper/2608.28884/) (2026)

## Abstract (arXiv)

This paper describes an implementation of a bot assistant in Minecraft, and the tools and platform allowing players to interact with the bot and to record those interactions. The purpose of building such an assistant is to facilitate the study of agents that can complete tasks specified by dialogue, and eventually, to learn from dialogue interactions.