用轻量语言模型实现机器人多点导航，响应快精度高。
FASTNav: Fine-tuned Adaptive Small-language-models Trained for Multi-point Robot Navigation
- 微调+师生迭代，提升小模型导航能力
- 实测响应快、精度高，支持边缘部署
- 适合资源受限场景的智能机器人导航
随着大语言模型（LLM）的快速发展，机器人开始受益于其带来的新型交互方式。由于边缘计算满足快速响应、隐私保护和网络自主性需求，我们认为它有助于在各行业中广泛部署大模型进行机器人导航。为实现语言模型在边缘设备上的本地部署，我们采用若干模型增强方法。本文提出FASTNav——一种用于机器人多点导航的轻量级语言模型（SLM）增强方法。该方法包含三个模块：微调、师生迭代和基于语言的多点导航。我们在仿真环境和真实机器人上训练并评估了模型，证明其可实现低成本、高精度和低延迟的部署。相较于其他模型压缩方法，FASTNav在本地部署语言模型方面展现出潜力，有望成为边缘设备上语言引导机器人导航的可行方案。
原文摘要 · Abstract (English)
With the rapid development of large language models (LLM), robots are starting to enjoy the benefits of new interaction methods that large language models bring. Because edge computing fulfills the needs for rapid response, privacy, and network autonomy, we believe it facilitates the extensive deployment of large models for robot navigation across various industries. To enable local deployment of language models on edge devices, we adopt some model boosting methods. In this paper, we propose FASTNav - a method for boosting lightweight LLMs, also known as small language models (SLMs), for robot navigation. The proposed method contains three modules: fine-tuning, teacher-student iteration, and language-based multi-point robot navigation. We train and evaluate models with FASTNav in both simulation and real robots, proving that we can deploy them with low cost, high accuracy and low response time. Compared to other model compression methods, FASTNav shows potential in the local deployment of language models and tends to be a promising solution for language-guided robot navigation on edge devices.
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