Do We Really Need a Complex Agent System? Distill Embodied Agent into a Single Model

• URL: http://arxiv.org/abs/2404.04619v1
• Date: Sat, 6 Apr 2024 12:51:00 GMT
• Title: Do We Really Need a Complex Agent System? Distill Embodied Agent into a Single Model
• Authors: Zhonghan Zhao, Ke Ma, Wenhao Chai, Xuan Wang, Kewei Chen, Dongxu Guo, Yanting Zhang, Hongwei Wang, Gaoang Wang,
• Abstract summary: We propose STEVE-2, a hierarchical knowledge distillation framework for open-ended embodied tasks. After distillation, embodied agents can complete complex, open-ended tasks without additional expert guidance.
• Score: 15.558269067931374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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