Foresight social-aware reinforcement learning for robot navigation

When robots handle navigation tasks while avoiding collisions, they perform in crowded and complex environments not as good as in stable and omogeneous environments. This often results in a low success rate and poor efficiency. Therefore, we propose a novel Foresight Social-aware Reinforcement Learning (FSRL) framework for mobile robots to achieve collisionfree navigation. Compared to previous learning-based methods, our approach is foresighted. It not only considers the current human-robot interaction to avoid an immediate collision, but also estimates upcoming social interactions to still keep distance in the future. Furthermore, an efficiency constraint is introduced in our approach that significantly reduces navigation time. Comparative experiments are performed to verify the effectiveness and efficiency of our proposed method under more realistic and challenging simulated environments.