人混み迷路を突破：生成的模倣学習によるリアルタイムロボット経路計画
Navigating the Human Maze: Real-Time Robot Pathfinding with Generative Imitation Learning
群衆の行動を生成する目標条件付き自己回帰モデルとサンプリングベースMPCを組み合わせ、混雑環境でロボットが先読みしながらリアルタイムに経路計画する手法を提案した。
著者: Martin Moder, Stephen Adhisaputra, Josef Pauli
分類: cs.RO, cs.AI
原文アブストラクト
This paper addresses navigation in crowded environments by integrating goal-conditioned generative models with Sampling-based Model Predictive Control (SMPC). We introduce goal-conditioned autoregressive models to generate crowd behaviors, capturing intricate interactions among individuals. The model processes potential robot trajectory samples and predicts the reactions of surrounding individuals, enabling proactive robotic navigation in complex scenarios. Extensive experiments show that this algorithm enables real-time navigation, significantly reducing collision rates and path lengths, and outperforming selected baseline methods. The practical effectiveness of this algorithm is validated on an actual robotic platform, demonstrating its capability in dynamic settings.