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[Submitted on 29 Jul 2026] Title:SAFE: Unified Slip and Fracture Detection with Low-Cost Acoustic Sensing in Robotic Grasping View a PDF of the paper titled SAFE: Unified Slip and Fracture Detection with Low-Cost Acoustic Sensing in Robotic Grasping, by Zerun Wang and 2 other authors View PDF HTML (experimental) Abstract:Manipulating fragile objects remains challenging as robots must understand the state of what they grasp, such as slip or fracture, to respond appropriately, especially when material properties are unknown. In this paper, we present SAFE: a low-cost, general-purpose sensing approach that detects both slip and fracture in real time using two passive polyvinylidene fluoride (PVDF) acoustic sensors and motor proprioception, without relying on vision or prior material knowledge. The sensors are mounted on a compliant Fin Ray gripper, and a unified HistGradientBoosting classifier reports the state (normal, slip, or fracture) from a 79-dimensional feature vector. Under leave-one-grasp-out cross-validation, SAFE achieves an Alert-F1 of 0.884 with near-zero slip-fracture confusion, and ablations confirm that acoustic sensing is indispensable. An adaptive grasp controller built on this detection layer runs at 104 Hz on a Jetson Orin Nano, achieving 91.3% success across 46 closed-loop robot trials spanning diverse object categories, while each fixed-force strategy drops to 0% on object conditions that mismatch its preset. It further reaches 82.4% success on novel objects unseen during training, demonstrating robust, failure-aware grasp control without object-specific calibration. Comments: 8 pages, 6 figures, 5 tables Subjects: Robotics (cs.RO) Cite as: arXiv:2610.08802 [cs.RO] (or arXiv:2610.08802v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2610.08802 arXiv-issued DOI via DataCite Submission history From: Zerun Wang [view email] [v1] Wed, 29 Jul 2026 17:46:12 UTC (6,165 KB) Full-text links: Access Paper: View a PDF of the paper titled SAFE: Unified Slip and Fracture Detection with Low-Cost Acoustic Sensing in Robotic Grasping, by Zerun Wang and 2 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.RO new | recent | 2026-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)