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

Tjomsland J., Kalkan S., Gunes H.

FRONTIERS IN ROBOTICS AND AI, cilt.9, 2022 (ESCI) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 9
  • Basım Tarihi: 2022
  • Doi Numarası: 10.3389/frobt.2022.669420
  • Dergi Adı: FRONTIERS IN ROBOTICS AND AI
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, INSPEC, Directory of Open Access Journals
  • Anahtar Kelimeler: human-robot interaction, social appropriateness, domestic robots, lifelong learning, Bayesian neural network
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.