Mind Your Manners! A Dataset and A Continual Learning Approach for Assessing Social Appropriateness of Robot Actions
Publication Date
2022Journal Title
Frontiers in Robotics and AI
ISSN
2296-9144
Publisher
Frontiers Media
Volume
9
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Tjomsland, J., Kalkan, S., & Gunes, H. (2022). Mind Your Manners! A Dataset and A Continual Learning Approach for
Assessing Social Appropriateness of Robot Actions. Frontiers in Robotics and AI, 9 https://doi.org/10.3389/frobt.2022.669420
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. To
be able to control but vary the configurations of the scenes and the social
settings, MANNERS-DB has been created utilising a simulation environment by
uniformly sampling relevant contextual attributes. Secondly, we train and
evaluate a baseline Bayesian Neural Network (BNN) that estimates social
appropriateness of actions in the MANNERS-DB. Finally, we formulate learning
social appropriateness of actions as a continual learning problem using the
uncertainty of the BNN parameters. The experimental results show that the
social appropriateness of robot actions can be predicted with a satisfactory
level of precision. Our work takes robots one step closer to a human-like
understanding of (social) appropriateness of actions, with respect to the
social context they operate in. To facilitate reproducibility and further
progress in this area, the MANNERS-DB, the trained models and the relevant code
will be made publicly available.
Keywords
Robotics and AI, human-robot interaction, social appropriateness, domestic robots, lifelong learning, Bayesian neural network
Sponsorship
Engineering and Physical Sciences Research Council (EP/R030782/1)
Identifiers
669420
External DOI: https://doi.org/10.3389/frobt.2022.669420
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335440
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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