教師なし学習で探る都市環境でのロボットと歩行者のインタラクション
Using Unsupervised Learning to Explore Robot-Pedestrian Interactions in Urban Environments
都市部でのロボットと歩行者の衝突シナリオを対象に、PCAとK-meansでインタラクションデータのパターンを抽出するパイプラインを提案し、実データで検証した。
著者: Sebastian Zug, Georg Jäger, Norman Seyffer, Martin Plank, Gero Licht, Felix Wilhelm Siebert
分類: cs.RO
原文アブストラクト
This study identifies a gap in data-driven approaches to robot-centric pedestrian interactions and proposes a corresponding pipeline. The pipeline utilizes unsupervised learning techniques to identify patterns in interaction data of urban environments, specifically focusing on conflict scenarios. Analyzed features include the robot's and pedestrian's speed and contextual parameters such as proximity to intersections. They are extracted and reduced in dimensionality using Principal Component Analysis (PCA). Finally, K-means clustering is employed to uncover underlying patterns in the interaction data. A use case application of the pipeline is presented, utilizing real-world robot mission data from a mid-sized German city. The results indicate the need for enriching interaction representations with contextual information to enable fine-grained analysis and reasoning. Nevertheless, they also highlight the need for expanding the data set and incorporating additional contextual factors to enhance the robots situational awareness and interaction quality.
