Affective Mechanisms for Modelling Lifelong Human-Robot Relationships

随着机器人成为人类生活中不可或缺的一部分，重要的是它们必须配备增强的交互能力。因此，社交机器人的人-机器人交互(HRI)研究获得了势头，研究人员专注于使这些交互尽可能顺畅和自然。对机器人来说，重要的是成为其人类环境的自然延伸，使它们能够重复地与用户进行广泛的交互。情商(EIQ)是人与人之间互动的核心，增加了意义和背景。因此，情商对于自然主义和引人入胜的人-机器人交互是必不可少的，使机器人能够调整它们的反应，并为用户提供个性化的交互体验。尽管目前的大多数HRI研究都在机器人中嵌入了情感识别能力，但它们依赖于基于帧的绝对注释。这些仅限于少数几个相互脱节的情感类别，如愤怒、快乐或悲伤，它们之间几乎没有重叠。当我们观察人类如何相互作用和表达情感时，这种关于情感的宽泛概括似乎是违反直觉的。人类的情绪会随着时间的推移而发展，并随着个人、交往伙伴或环境的不同而变化。因此，采用一种连续的情绪观点是有益的，它允许我们绘制价态(情绪的积极或消极性质)以及其强度，提供更平稳的过渡。以一种发展和演变的方式对情绪进行建模也很重要，在这种方式下，随着时间的推移，一系列评估产生了互动中情感背景的稳健模型。这种情感理解将使机器人能够形成内在的情感反应，作为对其在交互中的状态的评估。在这些评估的基础上，它应该学会在各种人力资源倡议情景下执行不同任务的同时与用户互动。为了解决这些悬而未决的问题，这项研究将专注于使用深层和混合神经体系结构来建模人类和同伴机器人之间的长期关系。多通道情绪感知技术将结合视觉和语音等多种通道来设计。机器人应该利用这种感知，通过监测用户的反应，逐步学习其与用户交互的情感背景。对这种情感相互作用的短期和长期影响进行建模的进化神经表示应构成学习在不同环境条件下的最佳行为的基础。这种理解也应随着机器人与不同用户的互动而发展，从而概括其在此过程中的学习。应研究新的强化学习机制，以实现机器人行为在各种HRI环境中的终身适应。在与不同的用户群体互动时，机器人应该学会帮助/指导他们执行复杂的认知任务，如玩协作/竞技游戏进行认知训练，同时关注他们的心理健康和认知发展。这项研究与主要主管的EPSRC授权自适应机器人幸福情商(ARoEQ)保持一致，旨在开发一个全面的自主系统，用于情感理解和机器人行为建模，试图摆脱绿野仙踪的方法。为同伴机器人配备这样的情感理解将使它们能够利用情感交互能力让用户参与认知任务。在情感计算核心原则的启发下，这个博士项目将(I)弥合依赖特征的计算模型与对人类因素的更深心理和认知理解之间的差距，以及(Ii)建立一个在机器人中实现情感行为的整体模型，以帮助人类。

As robots become an integral part of human life, it is important that they are equipped with enhanced interaction capabilities. Human-Robot Interaction (HRI) research for Social Robots has thus gained momentum with researchers focussing on making these interactions as smooth and natural as possible. It is important for robots to become natural extensions to their human environment, allowing them to hold extended interactions with users, repeatedly. Emotional Intelligence (EIQ) is central to human-human interactions, adding meaning and context. EIQ is therefore indispensable for naturalistic and engaging human-robot interactions, enabling robots to adapt their responses and provide their users with personalised interaction experiences.Although most of the current HRI studies embed emotion recognition capabilities in robots, they rely on frame-based absolute annotations. These are limited to a handful of disjointed emotional categories such as anger, happiness or sadness, with little to no overlap amongst them. This broad generalisation with respect to emotions seems counter-intuitive when we look at how humans interact with each other and express emotions. Human emotions develop over time and vary with individuals, interaction partners or environments. It is thus beneficial to adopt a continuous view of emotions which allows us to map the valence (the positive or negative nature of an emotion), as well as its intensity, providing smoother transitions. It is also important to model emotions in a developmental and evolving manner where a series of evaluations over time yield a robust model of the affective context in an interaction. This emotional understanding will enable robots to form intrinsic affective responses as an evaluation of its state in the interaction. Based on these evaluations, it shall learn to interact with users while performing different tasks under various HRI scenarios. To address these open questions, this research will focus on modelling long-term relationships between humans and companion robots using deep and hybrid neural architectures. Multi-modal emotion perception techniques will be devised combining multiple modalities such as vision and speech. The robot shall use this perception to incrementally learn the emotional context of its interaction with users by monitoring their responses. Evolving neural representations that model short-term as well as the long-term impact of such an affective interaction shall form the basis for learning optimal behaviour under different environmental conditions. This understanding shall also develop as the robot interacts with different users, generalising its learning in the process. New reinforcement learning mechanisms shall be investigated to achieve lifelong adaptation of robot behaviour in various HRI contexts. Interacting with different user groups, the robot shall learn to assist/coach them in performing complex cognitive tasks such as playing collaborative/competitive games for cognitive training while focusing on their mental health and cognitive development.This research, aligning itself with the primary supervisor's EPSRC grant Adaptive Robotic EQ for Well-being (ARoEQ), aims to develop a holistic and autonomous system for emotional understanding and robot behaviour modelling, attempting to move away from Wizard-of-Oz approaches. Equipping companion robots with such an affective understanding will enable them to engage users in cognitive tasks using affective interaction capabilities. Inspired by the central principles of affective computing, this PhD project shall (i) bridge the gap between feature-dependent computational models and the deeper psychological and cognitive understanding of human factors and (ii) build a holistic model for actualising affective behaviour in robots for assisting humans.