Optimus-3: Towards Generalist Multimodal Minecraft Agents with Scalable
  Task Experts
Authors: name: Zaijing Li, name: Yuquan Xie, name: Rui Shao, name: Gongwei Chen, name: Weili Guan, name: Dongmei Jiang, name: Liqiang Nie

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/