APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World Agents

• URL: http://arxiv.org/abs/2411.17255v1
• Date: Tue, 26 Nov 2024 09:31:28 GMT
• Title: APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World Agents
• Authors: Jun Yu Chen, Tao Gao,
• Abstract summary: We present an advanced Large Language Model (LLM)-driven framework that enables autonomous agents to construct complex structures in Minecraft. By employing chain-of-thought decomposition along with multimodal inputs, the framework generates detailed architectural layouts and blueprints. Our agent incorporates both memory and reflection modules to facilitate lifelong learning, adaptive refinement, and error correction throughout the building process.
• Score: 8.479128275067742
• License:
• Abstract: We present APT, an advanced Large Language Model (LLM)-driven framework that enables autonomous agents to construct complex and creative structures within the Minecraft environment. Unlike previous approaches that primarily concentrate on skill-based open-world tasks or rely on image-based diffusion models for generating voxel-based structures, our method leverages the intrinsic spatial reasoning capabilities of LLMs. By employing chain-of-thought decomposition along with multimodal inputs, the framework generates detailed architectural layouts and blueprints that the agent can execute under zero-shot or few-shot learning scenarios. Our agent incorporates both memory and reflection modules to facilitate lifelong learning, adaptive refinement, and error correction throughout the building process. To rigorously evaluate the agent's performance in this emerging research area, we introduce a comprehensive benchmark consisting of diverse construction tasks designed to test creativity, spatial reasoning, adherence to in-game rules, and the effective integration of multimodal instructions. Experimental results using various GPT-based LLM backends and agent configurations demonstrate the agent's capacity to accurately interpret extensive instructions involving numerous items, their positions, and orientations. The agent successfully produces complex structures complete with internal functionalities such as Redstone-powered systems. A/B testing indicates that the inclusion of a memory module leads to a significant increase in performance, emphasizing its role in enabling continuous learning and the reuse of accumulated experience. Additionally, the agent's unexpected emergence of scaffolding behavior highlights the potential of future LLM-driven agents to utilize subroutine planning and leverage the emergence ability of LLMs to autonomously develop human-like problem-solving techniques.

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