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Extending the abilities of service robots is important for expanding whatthey can achieve in everyday manipulation tasks. On the other hand, it is alsoessential to ensure them to determine what they can not achieve in certaincases due to either anomalies or permanent failures during task execution.Robots need to identify these situations, and reveal the reasons behind thesecases to overcome and recover from them. In this paper, we propose and analyzea Long Short-Term Memories-based (LSTM-based) awareness approach to reveal thereasons behind an anomaly case that occurs during a manipulation episode in anunstructured environment. The proposed method takes into account the real-timeobservations of the robot by fusing visual, auditory and proprioceptive sensorymodalities to achieve this task. We also provide a comparative analysis of ourmethod with Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs).The symptoms of anomalies are first learned from a given training set, thenthey can be classified in real-time based on the learned models. The approachesare evaluated on a Baxter robot executing object manipulation scenarios. Theresults indicate that the LSTM-based method outperforms the other methods witha 0.94 classification rate in revealing causes of anomalies in case of anunexpected deviation.

What went wrong?: Identification of Everyday Object Manipulation Anomalies