Computational Behaviour Generation for a Robotic Coach for Well-being

社交机器人正在不断地被研究和应用到新的领域，并为医院，教室和家庭等复杂的社会环境开发应用程序。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，以提高其流畅性，实用性和适应能力。人与人之间的社会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他们感觉更自然地成为人类的互动伙伴。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，因为它们有可能使机器人对其用户表达同情。社交机器人以前曾被研究为提高幸福感的工具，例如自闭症儿童的治疗和中风康复。具有EIQ的机器人作为在教练背景下增加和保持福祉的工具是一个未经审查的研究领域。研究表明，教练可以在保持主观和心理健康，预防心理健康问题方面发挥作用。通过创建一个利用自适应情感的机器人教练（即根据正在进行的交互调整其情感表达的机器人），新技术可以用于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自我教练和与人类教练合作的现有挑战，例如缺乏动力和尴尬的问题。为了在教练环境中应用EIQ，机器人将采用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（显示情感）机器人行为的模型。我们的目标是使机器人能够适应自己的情绪状态，随着时间的推移，在HRI的互动，通过感知教练的即时情绪状态，以及通过创建一个模型的正在进行的互动和它的情绪背景。为了构建这样的模型，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HRI中哪些行为是有用的，以及（2）了解机器人教练对用户健康的影响。该研究旨在与专家（如心理学家和潜在的教练）合作，应用参与式设计方法来了解什么类型的机器人和HRI场景最有用。参与式设计过程的结果将适用于创建适当的HRI方案，纳入环境影响系数，并检查其效果。

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.