What went wrong? Identification of everyday object manipulation anomalies

نویسندگان

چکیده

Enhancing the abilities of service robots is important for expanding what they can achieve in everyday manipulation tasks. In addition, it also essential to ensure that determine cannot achieve. Such necessity may arise due anomalies during task execution. These situations should be detected and identified overcome recover from them. Identification necessitates a deeper time series analysis onboard sensor readings keep track relate anomaly indicators since some perceived long before detection an anomaly. are usually taken asynchronously need fused effectively correct interpretations. this paper, we propose multimodal short-term memory-based (LSTM-based) identification approach takes into account real-time observations by fusing visual, auditory proprioceptive sensory modalities object The symptoms first trained then classified based on learned models real time. We provide comparative our method with hidden Markov (HMMs), conditional random fields (CRFs) gated recurring units (GRUs) Baxter robot executing scenarios. Then, analyze impact each modality various feature extraction techniques performance problem. show has ability identify capturing long-term dependencies between indicators. results indicate LSTM-based outperforms closest baseline 2% improvement f-score (0.92) classifying occur run-time.

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ژورنال

عنوان ژورنال: Intelligent Service Robotics

سال: 2021

ISSN: ['1861-2784', '1861-2776']

DOI: https://doi.org/10.1007/s11370-021-00355-w