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We present a novel human-aware navigation approach, where the robot learns tomimic humans to navigate safely in crowds. The presented model, referred to asDeepMoTIon, is trained with pedestrian surveillance data to predict humanvelocity in the environment. The robot processes LiDAR scans via the trainednetwork to navigate to the target location. We conduct extensive experiments toassess the components of our network and prove their necessity to imitatehumans. Our experiments show that DeepMoTIion outperforms all the benchmarks interms of human imitation, achieving a 24% reduction in time series-based pathdeviation over the next best approach. In addition, while many other approachesoften failed to reach the target, our method reached the target in 100% of thetest cases while complying with social norms and ensuring human safety.

DeepMoTIon: Learning to Navigate Like Humans