A Study on Domain Generalization for Failure Detection through Human Reactions in HRI

• URL: http://arxiv.org/abs/2403.06315v1
• Date: Sun, 10 Mar 2024 21:30:22 GMT
• Title: A Study on Domain Generalization for Failure Detection through Human Reactions in HRI
• Authors: Maria Teresa Parreira, Sukruth Gowdru Lingaraju, Adolfo Ramirez-Aristizabal, Manaswi Saha, Michael Kuniavsky, Wendy Ju
• Abstract summary: Machine learning models are commonly tested in-distribution (same dataset); performance almost always drops in out-of-distribution settings. This makes domain generalization - retaining performance in different settings - a critical issue. We present a concise analysis of domain generalization in failure detection models trained on human facial expressions.
• Score: 7.664159325276515
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning models are commonly tested in-distribution (same dataset); performance almost always drops in out-of-distribution settings. For HRI research, the goal is often to develop generalized models. This makes domain generalization - retaining performance in different settings - a critical issue. In this study, we present a concise analysis of domain generalization in failure detection models trained on human facial expressions. Using two distinct datasets of humans reacting to videos where error occurs, one from a controlled lab setting and another collected online, we trained deep learning models on each dataset. When testing these models on the alternate dataset, we observed a significant performance drop. We reflect on the causes for the observed model behavior and leave recommendations. This work emphasizes the need for HRI research focusing on improving model robustness and real-life applicability.

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