论文标题
通过在人机合作中不做错的做法做对
Doing Right by Not Doing Wrong in Human-Robot Collaboration
论文作者
论文摘要
随着机器人系统变得越来越有能力帮助人类的日常生活，我们必须考虑这些人造代理人使他们的人类合作者感到不安全或不公平地对待他们的机会。机器人可以表现出反社会行为，从而造成人身伤害或再现不公平的行为复制，甚至扩大对与人类相互作用的人类有害的历史和社会偏见。在本文中，我们讨论了考虑社交机器人操纵和公平机器人决策的这些问题。我们提出了一种新颖的学习公平和社交行为的方法，而不是通过重现积极行为，而是通过避免负面行为。在这项研究中，我们强调了将社交性纳入机器人操纵中的重要性，以及考虑在人类机器人相互作用中公平性的必要性。
As robotic systems become more and more capable of assisting humans in their everyday lives, we must consider the opportunities for these artificial agents to make their human collaborators feel unsafe or to treat them unfairly. Robots can exhibit antisocial behavior causing physical harm to people or reproduce unfair behavior replicating and even amplifying historical and societal biases which are detrimental to humans they interact with. In this paper, we discuss these issues considering sociable robotic manipulation and fair robotic decision making. We propose a novel approach to learning fair and sociable behavior, not by reproducing positive behavior, but rather by avoiding negative behavior. In this study, we highlight the importance of incorporating sociability in robot manipulation, as well as the need to consider fairness in human-robot interactions.
