In environments where humans coexist with mobile robots, there is a desire to improve both safety and efficiency of movement. If the impact of a robot’s actions on humans can be anticipated in advance, mobile robots should be able to intentionally choose the optimal actions for both themselves and humans. In this paper, we propose a machine learning approach to predict how pedestrians’ trajectories change based on the future paths selected by mobile robots.