Computational Behaviour Generation for a Robotic Coach for Well-being

社交机器人正在不断地研究和应用于新的领域，正在开发适用于医院、教室和家庭等社交复杂环境的应用程序。构建这些类型的应用程序需要不断研究社交机器人的交互和社交能力，以增加它们的流畅性、实用性和适应能力。人与人之间的社交互动的核心部分是情感的感知和表达。这使得情绪智力(EIQ)成为人-机器人交互(HRI)领域的一个重要研究领域，因为赋予机器人情感技能可以帮助它们作为人类的交互伙伴感到更自然。在旨在提高人类幸福感的应用中，研究机器人的情感行为非常重要，因为它们具有使机器人向用户表达同理心的潜力。以前，社交机器人被研究为增加幸福感的工具，例如用于治疗自闭症儿童，以及中风康复。在教练的背景下，将EIQ作为增加和保持幸福感的工具的机器人是一个未经检验的研究领域。研究表明，教练可以在保持主观和心理健康方面发挥作用，并预防心理健康问题。通过创造一种利用自适应情感的机器人教练(即根据正在进行的互动来调整其情感表达的机器人)，新技术可以用来改善人们的福祉，并保持劳动力的力量。机器人教练可以用来解决现有的挑战，既可以自我教练，也可以与人类教练合作，例如缺乏动力和尴尬的问题。为了将情商应用于教练环境，机器人将结合最先进的情感计算和机器学习方法。这项研究的计算挑战将是创建一个自适应情感(展示情感)机器人行为的模型。目标是使机器人能够在HRI互动期间随着时间的推移调整自己的情绪状态，通过感知被辅导者的即时情绪状态，以及通过创建正在进行的互动及其情绪环境的模型。为了建立这样的模型，将比较不同的机器学习方法，如强化学习、贝叶斯方法和马尔可夫模型的适用性。研究将集中在(1)了解在教练环境中哪些行为在HRI中是有用的，以及(2)了解机器人教练对用户幸福感的影响。这项研究旨在与专家(如心理学家和潜在的受训者)合作，应用参与式设计方法来了解哪种类型的机器人和HRI场景最有用。参与式设计过程的结果将被应用于创建纳入EIQ的适当的HRI情景，并检查其影响。

Social robots are continuously being researched and applied to new domains, with applications being developed for socially complex environments such as hospitals, classrooms, and homes. Building these types of applications requires that social robots' interaction and social capabilities be continuously researched to increase their smoothness, usefulness and adaptation capabilities. A central part of human-to-human social interaction is the sensing and expression of emotions. This makes Emotional Intelligence (EIQ) an important research area in the field of Human-Robot Interaction (HRI), as endowing robots with emotion skills can help make them feel more natural as interaction partners to humans. In applications which aim to increase human well-being, researching affective behaviours for robots is important, due to their potential to enable a robot to express empathy toward its user.Social robots have been previously researched as tools to increase well-being, such as in therapy with children with autism, and in stroke rehabilitation. Robots with EIQ as tools to increase and maintain well-being in the context of coaching is an unexamined research area. Research shows that coaching can have a role in maintaining subjective and psychological well-being, and preventing mental health issues. By creating a robotic coach that utilizes adaptive affect (i.e. a robot that adapts its emotional expression according to the ongoing interaction), new technology can be used to improve both people's well-being, and to maintain the strength of the workforce. A robotic coach could be used to address existing challenges with both self-coaching and working with a human coach, such as issues of lacking motivation and embarrassment. To apply EIQ in a coaching context, the robot will incorporate state-of-the-art methods of affective computing and machine learning. The computational challenge for this research will be creating a model for adaptive affective (displaying emotion) robot behaviour. The goal is to enable the robot to adapt its own emotional state over time during HRI interactions, by sensing the coachee's immediate emotional state, as well as by creating a model of the ongoing interaction and its emotional context. To build such a model, different methods of machine learning, such as Reinforcement Learning, Bayesian Methods, and Markov Models will be compared for suitability.The research will focus on (1) understanding what behaviours are useful in HRI in a coaching context, and (2) understanding the effects of a robotic coach on the user's well-being. The research aims to work together with experts (such as psychologists and potential coachees), applying participatory design methods to understand what type of robot and HRI scenarios would be most useful. Findings of the participatory design process will be applied to create appropriate HRI scenarios that incorporate EIQ, and examined for their effects.