  This work proposes a novel approach to social robot navigation by learning to
generate robot controls from a social motion latent space. By leveraging this
social motion latent space, the proposed method achieves significant
improvements in social navigation metrics such as success rate, navigation
time, and trajectory length while producing smoother (less jerk and angular
deviations) and more anticipatory trajectories. The superiority of the proposed
method is demonstrated through comparison with baseline models in various
scenarios. Additionally, the concept of humans' awareness towards the robot is
introduced into the social robot navigation framework, showing that
incorporating human awareness leads to shorter and smoother trajectories owing
to humans' ability to positively interact with the robot.

Robotics;Artificial Intelligence;Machine Learning