论文标题
介意你的举止！一种数据集和一种评估机器人行动社会适当性的学习方法
Mind Your Manners! A Dataset and A Continual Learning Approach for Assessing Social Appropriateness of Robot Actions
论文作者
论文摘要
迄今为止，不可能赋予具有评估其行为社会适当性的能力。这主要是由于（i）缺乏相关和标记的数据，以及（ii）缺乏作为终生学习问题的表述。在本文中，我们解决了这两个问题。我们首先介绍了具有社会适当的国内机器人动作数据集（Manners-DB），其中包含人类注释的机器人动作的适当标签。为了控制但可以改变场景和社交设置的配置，通过统一采样相关的上下文属性，通过模拟环境创建了Manners-DB。其次，我们培训和评估基线贝叶斯神经网络（BNN），该网络（BNN）估计了举止db中行动的社会适当性。最后，我们使用BNN参数的不确定性来将学习的社会适当性作为一个持续学习问题。实验结果表明，可以以令人满意的精度来预测机器人行动的社会适用性。我们的工作使机器人更接近人类对行动（社会）适当性的理解，就其运作的社会背景而言。为了促进可重复性和进一步的进展，Manners-DB，受过训练的模型和相关代码将公开可用。
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.
