Using Unsupervised Learning to Explore Robot-Pedestrian Interactions in Urban Environments

TL;DR

This paper presents an unsupervised learning pipeline to analyze robot-pedestrian interactions in urban environments, aiming to improve understanding and reasoning about conflict scenarios using real-world data.

Contribution

It introduces a novel unsupervised learning approach combining PCA and K-means clustering to analyze interaction patterns in robot-pedestrian data.

Findings

Identified key interaction patterns in urban robot-pedestrian data.

Highlighted the importance of contextual information for detailed analysis.

Showed the need for larger datasets and additional features for better situational awareness.

Abstract

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.