Imagine a world where robots seamlessly respond to our commands, not through complex code or button presses, but through the power of natural language. Researchers are making this a reality by fusing the intuitive nature of language with the precision of multi-robot control. A recent study demonstrates an innovative technique for training multiple robots to interpret and execute natural language navigation instructions. Using Large Language Models (LLMs), which have revolutionized how we interact with AI, the team translates human-like commands into a language robots can understand. But there’s a catch: LLMs can be computationally expensive, introducing latency that makes real-time control challenging, especially for teams of robots. This research tackles that problem head-on. Instead of placing the LLM directly in the control loop, the team uses it to create a 'latent space' – a sort of compressed representation of the commands. This space captures the essence of the instructions without the computational overhead. Offline reinforcement learning, a technique where robots learn from pre-recorded data, plays a crucial role. By observing the robots’ actions and outcomes from previous tasks, the system trains a model that quickly translates the command's latent representation into control signals. This offloading of LLM processing achieves remarkably low-latency control, allowing the robots to react and adapt quickly to changing scenarios and even each other’s movements. The team tested this method with a fleet of five robots, proving its ability to generalize instructions from short training data sets (as little as 20 minutes), even understanding never-before-seen commands. This points toward a future where robots can fluidly understand complex instructions like 'Agent 1, move to the top-left corner', or 'Agent 2, retrieve the blue box.' The implications are vast: from coordinating warehouse robots with voice commands to directing teams of rescue robots in disaster scenarios, natural language control holds immense potential. However, there are challenges to overcome. The current research focuses on navigation tasks in a controlled environment. Scaling to more diverse tasks, and to larger, more complex environments is a frontier for future work. Nevertheless, this research marks a significant stride towards a future where intuitive communication unlocks the full potential of multi-robot systems. It paints a picture of teams of robots working alongside humans, understanding and responding to our instructions with remarkable ease and precision.