小心你的Manners！机器人行为社会适当性评估的数据集和持续学习方法
Mind Your Manners! A Dataset and A Continual Learning Approach for Assessing Social Appropriateness of Robot Actions
摘要：
到目前为止，赋予机器人评估其行为的社会适当性的能力还不可能。这主要是由于：（一）缺乏相关和有标签的数据;（二）没有将这一问题作为终身学习问题加以阐述。在本文中，我们解决这两个问题。我们首先介绍了社会适当的家庭机器人动作数据集（MANNERS-DB），其中包含适当的标签，由人类注释的机器人动作。为了能够控制，但不同的场景和社会设置的配置，方式数据库已创建利用模拟环境，通过统一采样相关的上下文属性。其次，我们训练和评估的基线贝叶斯神经网络（BNN），估计社会适当的行动的方式数据库。最后，我们制定学习社会适当的行动作为一个持续学习的问题，使用的BNN参数的不确定性。实验结果表明，机器人动作的社会适当性可以预测具有令人满意的精度水平。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，这与它们所处的社会环境有关。为了促进这一领域的可重复性和进一步进展，MANNERS-DB、训练模型和相关代码将公开提供。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
