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Tjomsland J, Kalkan S, Gunes H. Mind Your Manners! A Dataset and a Continual Learning Approach for Assessing Social Appropriateness of Robot Actions. Front Robot AI 2022; 9:669420. [PMID: 35356061 PMCID: PMC8959540 DOI: 10.3389/frobt.2022.669420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 01/04/2022] [Indexed: 11/13/2022] Open
Abstract
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 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 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, MANNERS-DB, the trained models and the relevant code are made publicly available at https://github.com/jonastjoms/MANNERS-DB.
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Affiliation(s)
- Jonas Tjomsland
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Sinan Kalkan
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
- *Correspondence: Sinan Kalkan,
| | - Hatice Gunes
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
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