Imagine robots seamlessly navigating complex spaces, understanding your instructions, and doing it all without relying on the cloud. This futuristic scenario is closer than you think, thanks to innovative research on small language models (SLMs) and edge computing. Researchers are tackling the challenge of making robots smarter and more responsive by bringing the power of AI directly to the device, rather than relying on cloud-based large language models (LLMs). This approach not only speeds up processing but also safeguards privacy. One exciting project, FASTNav, focuses on boosting the abilities of SLMs for robot navigation. Using techniques like fine-tuning with domain-specific data and a clever teacher-student iteration process where a powerful LLM guides the smaller model, SLMs can achieve performance comparable to their larger counterparts. This approach allows robots to understand and execute complex, multi-step instructions in real-time. Testing in both simulated hospital environments and real-world settings like laboratory corridors has shown significant improvements in accuracy and efficiency. FASTNav is a promising leap forward, paving the way for a future where robots can interact more naturally and effectively within our world, while addressing critical considerations like privacy and responsiveness.