Reinforcement Learning (RL) has enabled social robots to generate trajectories without
human-designed rules or interventions, which makes it more effective than hard-coded …

Navigation lies at the core of social robotics, enabling robots to navigate and interact
seamlessly in human environments. The primary focus of human-aware robot navigation is …

In the dynamic construction industry, traditional robotic integration has primarily focused on
automating specific tasks, often overlooking the complexity and variability of human aspects …

Robot navigation in dense human crowds poses a significant challenge due to the
complexity of human behavior in dynamic and obstacle-rich environments. In this work, we …

Existing research on unconstrained in-the-wild head pose estimation suffers from the flaws
of its datasets, which consist of either numerous samples by non-realistic synthesis or …

Service robots in human-centered environments such as hospitals, office buildings, and long-
term care homes need to navigate while adhering to social norms to ensure the safety and …

The integration of robots into social environments necessitates their ability to interpret
human intentions and anticipate potential outcomes accurately. This capability is particularly …

Most existing mobile robotic datasets primarily capture static scenes, limiting their utility for
evaluating robotic performance in dynamic environments. To address this, we present a …

Human-Robot Collaboration (HRC) has evolved into a highly promising issue owing to the
latest breakthroughs in Artificial Intelligence (AI) and Human-Robot Interaction (HRI), among …

Navigating dynamic, human-populated environments is a critical challenge for mobile
robots, as they must balance effective pathfinding with minimizing social disruption. Cost …