未ラベル軌跡から対人配慮の経路補正を学習するLiDARベースsim2real GNNフレームワーク
Learning Interaction-Aware Path Corrections from Unlabeled Trajectories: A LiDAR-Based Sim-to-Real GNN Framework for Construction Robots
建設現場ロボット向けに、未ラベルの実演軌跡から歩行者との相互作用を考慮した経路補正を学習し、LiDARのみでリアルタイムにプランナ経路を補正するGNNフレームワークを提案した。
著者: Yilong Chen, Yong K. Cho
分類: cs.RO
原文アブストラクト
Construction robots operating near workers require navigation that can anticipate human motion while remaining robust to dynamic and unstructured site conditions. However, existing social navigation methods depend on hand-crafted interaction rules, labeled demonstrations, or robot-specific control policies, limiting their scalability and transferability in construction environments. We propose a real-time, LiDAR-only framework that learns interaction-aware path corrections from unlabeled demonstrations. Constant-velocity extrapolation generates residual learning targets, while a GNN models relationships among the robot, pedestrians, and goal context. During deployment, an extended Kalman filter tracks LiDAR detections to construct dynamic graphs, and the GNN offsets planner-generated waypoints through a planner-agnostic interface. Within the bricklaying trial, the GNN reduced path irregularity by 39.2\% and increased minimum TTC. Across Dynablox sequences, it increased minimum TTC by 38.3\% while producing shorter, smoother paths than the baselines. Ablation studies supported a 1.0-s prediction interval and confirmed the benefits of target context, reflection augmentation, and feature normalization.