  With the power of large language models (LLMs), open-ended embodied agents
can flexibly understand human instructions, generate interpretable guidance
strategies, and output executable actions. Nowadays, Multi-modal Language
Models~(MLMs) integrate multi-modal signals into LLMs, further bringing richer
perception to entity agents and allowing embodied agents to perceive
world-understanding tasks more delicately. However, existing works: 1) operate
independently by agents, each containing multiple LLMs, from perception to
action, resulting in gaps between complex tasks and execution; 2) train MLMs on
static data, struggling with dynamics in open-ended scenarios; 3) input prior
knowledge directly as prompts, suppressing application flexibility. We propose
STEVE-2, a hierarchical knowledge distillation framework for open-ended
embodied tasks, characterized by 1) a hierarchical system for multi-granular
task division, 2) a mirrored distillation method for parallel simulation data,
and 3) an extra expert model for bringing additional knowledge into parallel
simulation. After distillation, embodied agents can complete complex,
open-ended tasks without additional expert guidance, utilizing the performance
and knowledge of a versatile MLM. Extensive evaluations on navigation and
creation tasks highlight the superior performance of STEVE-2 in open-ended
tasks, with $1.4 \times$ - $7.3 \times$ in performance.
