MineDreamer: Learning to Follow Instructions via Chain-of-Imagination for Simulated-World Control

TL;DR

MineDreamer is an innovative embodied agent in Minecraft that uses a Chain-of-Imagination mechanism with multimodal models to follow complex instructions more accurately and reliably than previous methods.

Contribution

It introduces a novel Chain-of-Imagination approach combined with multimodal models to enhance instruction-following in simulated-world control tasks.

Findings

Significantly outperforms baseline agents in instruction-following accuracy.

Nearly doubles the performance of existing generalist agents.

Demonstrates strong generalization and understanding of the open world.

Abstract

It is a long-lasting goal to design a generalist-embodied agent that can follow diverse instructions in human-like ways. However, existing approaches often fail to steadily follow instructions due to difficulties in understanding abstract and sequential natural language instructions. To this end, we introduce MineDreamer, an open-ended embodied agent built upon the challenging Minecraft simulator with an innovative paradigm that enhances instruction-following ability in low-level control signal generation. Specifically, MineDreamer is developed on top of recent advances in Multimodal Large Language Models (MLLMs) and diffusion models, and we employ a Chain-of-Imagination (CoI) mechanism to envision the step-by-step process of executing instructions and translating imaginations into more precise visual prompts tailored to the current state; subsequently, the agent generates keyboard-and-mouse actions to efficiently achieve these imaginations, steadily following the instructions at each step. Extensive experiments demonstrate that MineDreamer follows single and multi-step instructions steadily, significantly outperforming the best generalist agent baseline and nearly doubling its performance. Moreover, qualitative analysis of the agent's imaginative ability reveals its generalization and comprehension of the open world.