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Safe manipulation in unstructured environments for service robots is achallenging problem. A failure detection system is needed to monitor and detectunintended outcomes. We propose FINO-Net, a novel multimodal sensor fusionbased deep neural network to detect and identify manipulation failures. We alsointroduce a multimodal dataset, containing 229 real-world manipulation datarecorded with a Baxter robot. Our network combines RGB, depth and audioreadings to effectively detect and classify failures. Results indicate thatfusing RGB with depth and audio modalities significantly improves theperformance. FINO-Net achieves 98.60% detection and 87.31% classificationaccuracy on our novel dataset. Code and data are publicly available athttps://github.com/ardai/fino-net.

FINO-Net: A Deep Multimodal Sensor Fusion Framework for Manipulation Failure Detection