  We present a novel human-aware navigation approach, where the robot learns to
mimic humans to navigate safely in crowds. The presented model, referred to as
DeepMoTIon, is trained with pedestrian surveillance data to predict human
velocity in the environment. The robot processes LiDAR scans via the trained
network to navigate to the target location. We conduct extensive experiments to
assess the components of our network and prove their necessity to imitate
humans. Our experiments show that DeepMoTIion outperforms all the benchmarks in
terms of human imitation, achieving a 24% reduction in time series-based path
deviation over the next best approach. In addition, while many other approaches
often failed to reach the target, our method reached the target in 100% of the
test cases while complying with social norms and ensuring human safety.
