Predicting Errors and Failures in Human-Robot Interaction from Multi-Modal Temporal Data

Abstract

Social robots should be able to detect social signals sent by their user, such as when the robot made a mistake or the user feels awkward. As our submission to the ERR@HRI challenge, we present a number of neural and traditional machine learning models to predict when this occurs in a human-robot conversation, based on facial expressions, body pose, and non-verbal speech characteristics. The small size of the dataset, imbalance in the label distribution, and low-frequency label annotations provided significant challenges. However, three of our approaches show promising results: modifying the training of a gated recurrent unit (GRU) model to predict at lower frequency than that of the input features, using an embedding layer and convolutional neural network to pre-process temporal data before feeding it to the GRU, and using traditional random forests.