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AURSAD: Universal Robot Screwdriving Anomaly Detection Dataset

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arxiv 2102.01409 v2 pith:55E562V4 submitted 2021-02-02 cs.LG cs.RO

B{l}ażej Leporowski , Daniella Tola , Casper Hansen , Alexandros Iosifidis This is my paper
classification cs.LGcs.RO
keywords robotscrewdrivingdatadatasetoperationprocessavailabledescribes
verification ladder T0 review T1 audit T2 compute T3 formal
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Screwdriving is one of the most popular industrial processes. As such, it is increasingly common to automate that procedure by using various robots. Even though the automation increases the efficiency of the screwdriving process, if the process is not monitored correctly, faults may occur during operation, which can impact the effectiveness and quality of assembly. Machine Learning (ML) has the potential to detect those undesirable events and limit their impact. In order to do so, first a dataset that fully describes the operation of an industrial robot performing automated screwdriving must be available. This report describes a dataset created using a UR3e series robot and OnRobot Screwdriver. We create different scenarios and introduce 4 types of anomalies to the process while all available robot and screwdriver sensors are continuously recorded. The resulting data contains 2042 samples of normal and anomalous robot operation. Brief ML benchmarks using this data are also provided, showcasing the data's suitability and potential for further analysis and experimentation.

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Cited by 4 Pith papers

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  1. FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models

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    FactoryNet is a 51M-point industrial time-series dataset with an S-E-F-C schema that supports zero-shot cross-embodiment transfer and competitive anomaly detection across robotic and machining tasks.

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    FactoryBench reveals that frontier LLMs achieve under 50% on structured causal questions and under 18% on decision-making in industrial robotic telemetry.

  4. COFFAIL: A Dataset of Successful and Anomalous Robot Skill Executions in the Context of Coffee Preparation

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    COFFAIL is a dataset of successful and anomalous robot skill executions in coffee preparation, used to demonstrate imitation learning of robot policies.

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