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LiDAR-based GNN framework learns interaction-aware path corrections from unlabeled trajectories for construction robots

The study proposes a real-time, LiDAR-only framework that learns interaction-aware path corrections from unlabeled demonstrations: constant-velocity extrapolation generates residual learning targets, a GNN models relationships among the robot, pedestrians, and goal context, and at deployment an extended Kalman filter tracks LiDAR detections to build dynamic graphs, with the GNN offsetting planner-generated waypoints through a planner-agnostic interface.

Part of the event: LiDAR-Based Label-Free Trajectory Path Correction Framework Proposed

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