PRISCA at ERR@HRI 2024: Multimodal Representation Learning for Detecting Interaction Ruptures in HRI

Abstract

Interaction ruptures in human-robot interaction (HRI) refer to scenarios when seamless interactions are disrupted. Such ruptures can be directly observed by the robot at times, e.g., not responding to a human utterance. However, often the ruptures could be more passive and subtle and require an analysis of the human’s behavior. In this work, we focus on detecting such ruptures by analyzing multimodal information in a face-to-face interaction setting. More specifically, this paper describes the PRISCA team’s participation in the ERR@HRI Challenge 2024, which was recently proposed to benchmark multimodal learning approaches to interaction rupture detection in HRI. Central to our approach is a feature-fusion strategy for multimodal representation learning, where we train a neural network with separate recurrent layers that act as temporal encoders to learn modality-specific representations. Our approach was ranked 3rd in the ERR@HRI challenge. We present detailed experimentation on the released dataset from the challenge and a thorough analysis of the results. We further discuss the limitations of current approaches and implications for future works. Code will be made available at https://github.com/pradippramanick/prisca-errhri/.