Optimus-3：迈向具备可扩展任务专家的通用型多模态《我的世界》智能体

摘要

近期，基于多模态大语言模型（MLLMs）的智能体在多个领域取得了显著进展。然而，在《我的世界》等开放世界环境中构建具备感知、规划、行动、定位和反思能力的通用智能体仍面临诸多挑战：领域特定数据不足、异构任务间的相互干扰，以及开放世界场景中的视觉多样性。本文通过三项关键贡献应对这些挑战：1）我们提出了一种知识增强的数据生成管道，为智能体开发提供可扩展且高质量的训练数据；2）为减轻异构任务间的干扰，我们引入了任务级路由的专家混合（MoE）架构；3）我们开发了一种多模态推理增强的强化学习方法，以提升智能体在《我的世界》中应对视觉多样性的推理能力。基于这些创新，我们推出了Optimus-3，一款面向《我的世界》的通用智能体。大量实验结果表明，Optimus-3在《我的世界》环境中的广泛任务上，均超越了通用多模态大语言模型及现有最先进的智能体。项目页面：https://cybertronagent.github.io/Optimus-3.github.io/

English

Recently, agents based on multimodal large language models (MLLMs) have achieved remarkable progress across various domains. However, building a generalist agent with capabilities such as perception, planning, action, grounding, and reflection in open-world environments like Minecraft remains challenges: insufficient domain-specific data, interference among heterogeneous tasks, and visual diversity in open-world settings. In this paper, we address these challenges through three key contributions. 1) We propose a knowledge-enhanced data generation pipeline to provide scalable and high-quality training data for agent development. 2) To mitigate interference among heterogeneous tasks, we introduce a Mixture-of-Experts (MoE) architecture with task-level routing. 3) We develop a Multimodal Reasoning-Augmented Reinforcement Learning approach to enhance the agent's reasoning ability for visual diversity in Minecraft. Built upon these innovations, we present Optimus-3, a general-purpose agent for Minecraft. Extensive experimental results demonstrate that Optimus-3 surpasses both generalist multimodal large language models and existing state-of-the-art agents across a wide range of tasks in the Minecraft environment. Project page: https://cybertronagent.github.io/Optimus-3.github.io/

Optimus-3：迈向具备可扩展任务专家的通用型多模态《我的世界》智能体

Optimus-3: Towards Generalist Multimodal Minecraft Agents with Scalable Task Experts

摘要

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