4 accepted papers
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…
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
Robots are becoming essential in human environments, requiring them to behave in a socially compliant manner. Although previous learning-based methods have shown potential in social navigation, most have treated pedestrians as individuals, failing to account for group level interactions. Additionall
Navigating unknown three-dimensional (3D) rugged environments is challenging for multi-robot systems. Traditional discrete systems struggle with rough terrain due to limited individual mobility, while modular systems—where rigid, controllable constraints link robot units—improve traversal but suffer