  We present a method for developing navigation policies for multi-robot teams
that interpret and follow natural language instructions. We condition these
policies on embeddings from pretrained Large Language Models (LLMs), and train
them via offline reinforcement learning with as little as 20 minutes of
randomly-collected data. Experiments on a team of five real robots show that
these policies generalize well to unseen commands, indicating an understanding
of the LLM latent space. Our method requires no simulators or environment
models, and produces low-latency control policies that can be deployed directly
to real robots without finetuning. We provide videos of our experiments at
https://sites.google.com/view/llm-marl.
