Mind Your Manners! A Dataset and a Continual Learning Approach for Assessing Social Appropriateness of Robot Actions

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2022-03-01

Author

Tjomsland, Jonas
Kalkan, Sinan
Gunes, Hatice

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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 (MANNE RS-DB), which contains appropriateness labels of robot actions annotated by humans. Secondly, we train and evaluate a baseline Multi Layer Perceptron and a Bayesian Neural Network (BNN) that estimate social appropriateness of actions in MANNE RS-DB. Finally, we formulate learning social appropriateness of actions as a continual learning problem using the uncertainty of Bayesian Neural Network 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, MANNE RS-DB, the trained models and the relevant code are made publicly available at https://github.com/ jonastjoms/MANNERS-DB.

Subject Keywords

human-robot interaction, social appropriateness, domestic robots, lifelong learning, Bayesian neural network

URI

https://hdl.handle.net/11511/97298

Journal

FRONTIERS IN ROBOTICS AND AI

DOI

https://doi.org/10.3389/frobt.2022.669420

Collections

Department of Computer Engineering, Article

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Citation Formats

J. Tjomsland, S. Kalkan, and H. Gunes, “Mind Your Manners! A Dataset and a Continual Learning Approach for Assessing Social Appropriateness of Robot Actions,” FRONTIERS IN ROBOTICS AND AI, vol. 9, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/97298.