Fail-RAG is a retrieval-augmented generation framework that detects and describes robot failures in warehouse tasks by querying an embedded failure database and applying VLMs, showing 25 percentage point higher accuracy than off-the-shelf VLMs.
Multimodal Anomaly Detection with a Mixture-of-Experts,
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Fail-RAG : A Retrieval Augmented Generation Informed Framework for Robot Failure Identification
Fail-RAG is a retrieval-augmented generation framework that detects and describes robot failures in warehouse tasks by querying an embedded failure database and applying VLMs, showing 25 percentage point higher accuracy than off-the-shelf VLMs.