Mind Your Manners! A Dataset and a Continual Learning Approach for Assessing Social Appropriateness of Robot Actions - Tjomsland, Jonas; Kalkan, Sinan; Gunes, Hatice (Frontiers in Robotics and AI, 2022)
To date, endowing robots with an ability to assess social appropriateness of their actions has not been possible. This has been mainly due to (i) the lack of relevant and labelled data, and (ii) the lack of formulations of this as a lifelong learning problem. In this paper, we address these two issues. We first introduce the Socially Appropriate Domestic Robot Actions dataset (MANNERS-DB), which contains appropriateness labels of robot actions annotated by humans. Secondly, we train and evaluate a baseline Multi Layer Perceptron (MLP) and a Bayesian Neural Network (BNN) that estimate social appropriateness of actions in MANNERS-DB. Finally, we formulate learning social appropriateness of actions as a continual learning problem using the uncertainty of BNN parameters. The experimental results show that the social appropriateness of robot actions can be predicted with a satisfactory level of precision. To facilitate reproducibility and further progress in this area, MANNERS-DB, the trained models and the relevant code will be made publicly available upon the publication of our work.