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arXiv · 2506.06174

Technical Report for Egocentric Mistake Detection for the HoloAssist Challenge

Constantin Patsch·Marsil Zakour·Yuankai Wu·Eckehard Steinbach
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Abstract

In this report, we address the task of online mistake detection, which is vital in domains like industrial automation and education, where real-time video analysis allows human operators to correct errors as they occur. While previous work focuses on procedural errors involving action order, broader error types must be addressed for real-world use. We introduce an online mistake detection framework that handles both procedural and execution errors (e.g., motor slips or tool misuse). Upon detecting an error, we use a large language model (LLM) to generate explanatory feedback. Experiments on the HoloAssist benchmark confirm the effectiveness of our approach, where our approach is placed second on the mistake detection task.

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Constantin Patsch, Marsil Zakour, Yuankai Wu, Eckehard Steinbach. 2025-06-06. Technical Report for Egocentric Mistake Detection for the HoloAssist Challenge. https://arxiv.org/abs/2506.06174

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