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

- Source: arXiv cs.RO
- Published: 2026-10-27T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/f01bxq8ezspjuerbh67vjsmw7
- Original: https://arxiv.org/abs/2610.34060

## Summary

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.