  With the increasing availability and affordability of personal robots, they
will no longer be confined to large corporate warehouses or factories but will
instead be expected to operate in less controlled environments alongside larger
groups of people. In addition to ensuring safety and efficiency, it is crucial
to minimize any negative psychological impact robots may have on humans and
follow unwritten social norms in these situations. Our research aims to develop
a model that can predict the movements of pedestrians and perceptually-social
groups in crowded environments. We introduce a new Social Group Long Short-term
Memory (SG-LSTM) model that models human groups and interactions in dense
environments using a socially-aware LSTM to produce more accurate trajectory
predictions. Our approach enables navigation algorithms to calculate
collision-free paths faster and more accurately in crowded environments.
Additionally, we also release a large video dataset with labeled pedestrian
groups for the broader social navigation community. We show comparisons with
different metrics on different datasets (ETH, Hotel, MOT15) and different
prediction approaches (LIN, LSTM, O-LSTM, S-LSTM) as well as runtime
performance.
