Autonomous service robots are increasingly necessary to move without impeding the movement of pedestrians. Previous studies have determined optimal input for robots by minimizing a multi-objective function that includes the cost of reaching the destination and avoiding surrounding pedestrians. However, it is challenging to adjust the weights of each term in the cost function since they depend on the users and environment. In this study, we used the Social Force Model (SFM) as the base cost function and proposed a method to estimate SFM weights preferred by general user based on population density from human feedback. To achieve this, first we use Bayesian optimization and derive each user’s evaluation map of SFM in a virtual reality environment that provides a realistic and immersive experience for subjects to provide feedback on the robot’s movement. Second, we aggregated each user’s evaluation map to estimate a general user’s evaluation map. Finally, we have derived a functional relationship between the preferred SFM weights of general users and population density by Gaussian process regression. This relationship empowers the robot to navigate in a manner preferred by the general public, contingent on population density, even in the absence of human feedback obtained through virtual reality experimentation.