It is a long-lasting goal to design an embodied system that can solve
long-horizon open-world tasks in human-like ways. However, existing approaches
usually struggle with compound difficulties caused by the logic-aware
decomposition and context-aware execution of these tasks. To this end, we
introduce MP5, an open-ended multimodal embodied system built upon the
challenging Minecraft simulator, which can decompose feasible sub-objectives,
design sophisticated situation-aware plans, and perform embodied action
control, with frequent communication with a goal-conditioned active perception
scheme. Specifically, MP5 is developed on top of recent advances in Multimodal
Large Language Models (MLLMs), and the system is modulated into functional
modules that can be scheduled and collaborated to ultimately solve pre-defined
context- and process-dependent tasks. Extensive experiments prove that MP5 can
achieve a 22% success rate on difficult process-dependent tasks and a 91%
success rate on tasks that heavily depend on the context. Moreover, MP5
exhibits a remarkable ability to address many open-ended tasks that are
entirely novel.

设计了一个使用多模态方法的体现系统，基于 Minecraft 仿真器，具备分解子目标、设计情境感知计划、执行体现动作控制以及与目标驱动的主动感知方案频繁通信的能力，在难度较大的任务中获得 22% 的成功率，在严重依赖背景的任务中获得 91% 的成功率，而且表现出在全新的多样任务上具备显著能力。