论文标题
机器人个性的情感驱动建模用于协作人类机器人互动
Affect-Driven Modelling of Robot Personality for Collaborative Human-Robot Interactions
论文作者
论文摘要
协作互动需要社会机器人来适应人类情感行为的动态。然而，当前机器人中情感行为产生的方法集中在瞬时感知上，以在观察到的人类表情和静态机器人动作之间产生一对一的映射。在本文中，我们为社交机器人中人格驱动的行为产生提出了一个新颖的框架。 The framework consists of (i) a hybrid neural model for evaluating facial expressions and speech, forming intrinsic affective representations in the robot, (ii) an Affective Core, that employs self-organising neural models to embed robot personality traits like patience and emotional actuation, and (iii) a Reinforcement Learning model that uses the robot's affective appraisal to learn interaction behaviour.为了进行评估，我们进行了一项用户研究（n = 31），其中NICO机器人在Ultimatum游戏中充当建议者。参与者见证了机器人个性对其谈判策略的影响，他们对持久性情绪高度驱动的患者机器人进行了排名，而惰性和不耐烦的机器人的慷慨和无私行为则更高。
Collaborative interactions require social robots to adapt to the dynamics of human affective behaviour. Yet, current approaches for affective behaviour generation in robots focus on instantaneous perception to generate a one-to-one mapping between observed human expressions and static robot actions. In this paper, we propose a novel framework for personality-driven behaviour generation in social robots. The framework consists of (i) a hybrid neural model for evaluating facial expressions and speech, forming intrinsic affective representations in the robot, (ii) an Affective Core, that employs self-organising neural models to embed robot personality traits like patience and emotional actuation, and (iii) a Reinforcement Learning model that uses the robot's affective appraisal to learn interaction behaviour. For evaluation, we conduct a user study (n = 31) where the NICO robot acts as a proposer in the Ultimatum Game. The effect of robot personality on its negotiation strategy is witnessed by participants, who rank a patient robot with high emotional actuation higher on persistence, while an inert and impatient robot higher on its generosity and altruistic behaviour.
