6 accepted papers
As autonomous mobile robots begin to populate public spaces, it is becoming increasingly important for robots to accurately distinguish pedestrians and navigate safely to avoid collisions. Texting while walking is a common but hazardous behavior among pedestrians that poses significant challenges fo…
Robot navigation in human-populated environments poses challenges due to the diversity of human behaviors and the unpredictability of human paths. However, existing Reinforcement Learning (RL)-based methods often rely on simulators that lack sufficient diversity in human behavior, resulting in navig…
As autonomous mobile robots begin to populate public spaces, it is becoming increasingly important for robots to accurately distinguish pedestrians and navigate safely to avoid collisions. Texting while walking is a common but hazardous behavior among pedestrians that poses significant challenges fo
Robot navigation in human-populated environments poses challenges due to the diversity of human behaviors and the unpredictability of human paths. However, existing Reinforcement Learning (RL)-based methods often rely on simulators that lack sufficient diversity in human behavior, resulting in navig
Mobile robot navigation in a human-populated environment has been of great interest to the research community in recent years, referred to as crowd navigation. Currently, offline reinforcement learning (RL)-based method has been introduced to this domain, for its ability to alleviate the sim2real ga…
Awareness of surrounding and prediction of dangerous situations is essential for autonomous mobile robots, especially during navigation in a human-populated environment. To cope with safety issues, state-of-the-art works have focused on pedestrian detection, tracking, and trajectory prediction. Howe