论文标题
情感机器人技术的持续学习：幸福的概念证明
Continual Learning for Affective Robotics: A Proof of Concept for Wellbeing
论文作者
论文摘要
维持现实世界中的人类机器人相互作用需要机器人对人类行为特质敏感，并适应其感知和行为模型以满足这些个人偏好。对于情感机器人，这需要学习适应个人情感行为，以为每个人提供个性化的互动体验。持续学习（CL）已被证明可以在代理商中实时适应，从而使他们能够通过逐步获得的数据学习，同时保留过去的知识。在这项工作中，我们提出了一个新颖的框架，用于使用基于CL的影响感知机制对个性化的人类机器人相互作用进行现实应用来建模。为了评估提出的框架，我们使用三种相互作用行为的变体与20名参与者进行概念证明的用户研究：静态和脚本化，使用基于情感的适应性而无需个性化，并使用基于情感的适应性进行持续个性化。我们的结果表明，参与者对基于CL的持续个性化的偏好明显偏爱，并且在机器人的拟人化，动画和可爱性等级中观察到的显着改善以及相互作用在温暖和舒适性方面的评分显着更高，因为机器人在理解参与者的感觉方面得到了显着评估。
Sustaining real-world human-robot interactions requires robots to be sensitive to human behavioural idiosyncrasies and adapt their perception and behaviour models to cater to these individual preferences. For affective robots, this entails learning to adapt to individual affective behaviour to offer a personalised interaction experience to each individual. Continual Learning (CL) has been shown to enable real-time adaptation in agents, allowing them to learn with incrementally acquired data while preserving past knowledge. In this work, we present a novel framework for real-world application of CL for modelling personalised human-robot interactions using a CL-based affect perception mechanism. To evaluate the proposed framework, we undertake a proof-of-concept user study with 20 participants interacting with the Pepper robot using three variants of interaction behaviour: static and scripted, using affect-based adaptation without personalisation, and using affect-based adaptation with continual personalisation. Our results demonstrate a clear preference in the participants for CL-based continual personalisation with significant improvements observed in the robot's anthropomorphism, animacy and likeability ratings as well as the interactions being rated significantly higher for warmth and comfort as the robot is rated as significantly better at understanding how the participants feel.
