  We propose an approach for mapping natural language instructions and raw
observations to continuous control of a quadcopter drone. Our model predicts
interpretable position-visitation distributions indicating where the agent
should go during execution and where it should stop, and uses the predicted
distributions to select the actions to execute. This two-step model
decomposition allows for simple and efficient training using a combination of
supervised learning and imitation learning. We evaluate our approach with a
realistic drone simulator, and demonstrate absolute task-completion accuracy
improvements of 16.85% over two state-of-the-art instruction-following methods.
