Foresight Social-aware Reinforcement Learning for Robot Navigation
This work addresses collision-free navigation for mobile robots in complex, crowded settings, representing an incremental improvement over prior learning-based methods.
The paper tackles robot navigation in crowded environments by proposing a Foresight Social-aware Reinforcement Learning (FSRL) framework that improves success rates and reduces navigation time through foresighted social interaction estimation and efficiency constraints.
When robots handle navigation tasks while avoiding collisions, they perform in crowded and complex environments not as good as in stable and homogeneous 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 collision-free 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.