Summary of Do We Really Need a Complex Agent System? Distill Embodied Agent Into a Single Model, by Zhonghan Zhao et al.
Do We Really Need a Complex Agent System? Distill Embodied Agent into a Single Model
by Zhonghan Zhao, Ke Ma, Wenhao Chai, Xuan Wang, Kewei Chen, Dongxu Guo, Yanting Zhang, Hongwei Wang, Gaoang Wang
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel hierarchical knowledge distillation framework called STEVE-2 is proposed for open-ended embodied tasks, which leverages large language models (LLMs) and multi-modal language models (MLMs). The framework addresses limitations of existing works by integrating LLMs with MLMs, enabling agents to perceive complex tasks more delicately. STEVE-2 comprises a hierarchical system for task division, mirrored distillation method for parallel simulation data, and an extra expert model to bring in additional knowledge. The framework allows embodied agents to complete open-ended tasks without expert guidance, utilizing the performance and knowledge of versatile MLMs. Evaluations on navigation and creation tasks demonstrate superior performance of STEVE-2, with a significant boost in performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Embodied agents can now understand human instructions, generate helpful advice, and take executable actions thanks to large language models (LLMs). To make things even better, multi-modal language models (MLMs) combine different signals to help these agents perceive the world more accurately. However, current approaches have some limitations: they work independently, use static data, or directly add prior knowledge as prompts. This makes it difficult for them to handle complex tasks. The new STEVE-2 framework helps overcome these issues by dividing tasks into smaller parts, simulating different scenarios, and adding expert knowledge. As a result, agents can complete tasks without needing further guidance. Tests show that STEVE-2 performs much better than existing approaches. |
Keywords
» Artificial intelligence » Distillation » Knowledge distillation » Multi modal
