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

Arda Inceoglu, Eren Erdal Aksoy, Abdullah Cihan Ak, Sanem Sariel
<span title="2021-07-30">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Safe manipulation in unstructured environments for service robots is a challenging problem. A failure detection system is needed to monitor and detect unintended outcomes. We propose FINO-Net, a novel multimodal sensor fusion based deep neural network to detect and identify manipulation failures. We also introduce a multimodal dataset, containing 229 real-world manipulation data recorded with a Baxter robot. Our network combines RGB, depth and audio readings to effectively detect and classify
more &raquo; ... ilures. Results indicate that fusing RGB with depth and audio modalities significantly improves the performance. FINO-Net achieves 98.60% detection and 87.31% classification accuracy on our novel dataset. Code and data are publicly available at
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:2011.05817v2</a> <a target="_blank" rel="external noopener" href="">fatcat:h7nyy7v7xzaylfnsowhygv6ct4</a> </span>
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