# Socially Aware Robot Navigation

Construction robots working near people must anticipate human motion, yet existing social-navigation methods rely on hand-crafted interaction rules, labeled demonstrations, or robot-specific control policies. This LiDAR-only framework generates residual targets automatically by constant-velocity extrapolation, models relations among robot, pedestrians and goal with a graph neural network, and uses an extended Kalman filter to turn frame-wise detections into dynamic graphs, outputting planner-agnostic path corrections. In a bricklaying trial it reduced path irregularity by 39.2% and raised minimum time-to-collision; across seven Dynablox sequences it raised average minimum time-to-collision by 38.3% with shorter, smoother paths than the baselines.

Construction robots working near people must anticipate human motion, yet existing social-navigation methods rely on hand-crafted interaction rules, labeled demonstrations, or robot-specific control policies. This LiDAR-only framework generates residual targets automatically by constant-velocity extrapolation, models relations among robot, pedestrians and goal with a graph neural network, and uses an extended Kalman filter to turn frame-wise detections into dynamic graphs, outputting planner-agnostic path corrections. In a bricklaying trial it reduced path irregularity by 39.2% and raised minimum time-to-collision; across seven Dynablox sequences it raised average minimum time-to-collision by 38.3% with shorter, smoother paths than the baselines.