  Robot navigation through crowds poses a difficult challenge to AI systems,
since the methods should result in fast and efficient movement but at the same
time are not allowed to compromise safety. Most approaches to date were focused
on the combination of pathfinding algorithms with machine learning for
pedestrian walking prediction. More recently, reinforcement learning techniques
have been proposed in the research literature. In this paper, we perform a
comparative evaluation of pathfinding/prediction and reinforcement learning
approaches on a crowd movement dataset collected from surveillance videos taken
at Grand Central Station in New York. The results demonstrate the strong
superiority of state-of-the-art reinforcement learning approaches over
pathfinding with state-of-the-art behaviour prediction techniques.
