  Robotic navigation through crowds or herds requires the ability to both
predict the future motion of nearby individuals and understand how these
predictions might change in response to a robot's future action. State of the
art trajectory prediction models using Recurrent Neural Networks (RNNs) do not
currently account for a planned future action of a robot, and so cannot predict
how an individual will move in response to a robot's planned path. We propose
an approach that adapts RNNs to use a robot's next planned action as an input
alongside the current position of nearby individuals. This allows the model to
learn the response of individuals with regards to a robot's motion from real
world observations. By linking a robot's actions to the response of those
around it in training, we show that we are able to not only improve prediction
accuracy in close range interactions, but also to predict the likely response
of surrounding individuals to simulated actions. This allows the use of the
model to simulate state transitions, without requiring any assumptions on agent
interaction. We apply this model to varied datasets, including crowds of
pedestrians interacting with vehicles and bicycles, and livestock interacting
with a robotic vehicle.
