Affective Mechanisms for Modelling Lifelong Human-Robot Relationships

建模终生人机关系的情感机制

• 批准号: 2107412
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2107412
• 关键词: Affective; Mechanisms; Modelling; Lifelong; Human

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.

随着机器人成为人类生活中不可或缺的一部分，它们配备增强的交互能力非常重要。因此，社交机器人的人机交互（HRI）研究获得了动力，研究人员致力于使这些交互尽可能顺畅和自然。对于机器人来说，成为人类环境的自然延伸非常重要，这使得它们能够与用户反复进行长时间的互动。情商 (EIQ) 是人与人互动的核心，可以增加意义和背景。因此，EIQ 对于自然且引人入胜的人机交互来说是不可或缺的，使机器人能够适应自己的反应并为用户提供个性化的交互体验。尽管当前大多数 HRI 研究都将情感识别功能嵌入到机器人中，但它们依赖于基于框架的绝对注释。这些仅限于少数杂乱的情绪类别，例如愤怒、快乐或悲伤，它们之间几乎没有重叠。当我们观察人类如何彼此互动和表达情感时，这种关于情感的广泛概括似乎是违反直觉的。人类的情绪随着时间的推移而发展，并随着个人、互动伙伴或环境的不同而变化。因此，采用连续的情绪观是有益的，它使我们能够绘制出价（情绪的积极或消极性质）及其强度，从而提供更平滑的过渡。以发展和演变的方式对情绪进行建模也很重要，随着时间的推移，一系列评估会产生交互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，作为对其交互状态的评估。基于这些评估，它应学习在各​​种 HRI 场景下执行不同任务时与用户交互。为了解决这些悬而未决的问题，这项研究将侧重于使用深度混合神经架构对人类和同伴机器人之间的长期关系进行建模。将结合视觉和语音等多种模态来设计多模态情感感知技术。机器人应利用这种感知，通过监控用户的反应来逐步了解其与用户交互的情感背景。模拟这种情感互动的短期和长期影响的进化神经表征将构成在不同环境条件下学习最佳行为的基础。随着机器人与不同用户的互动，这种理解也将得到发展，并在此过程中概括其学习。应研究新的强化学习机制，以实现机器人行为在各种 HRI 环境中的终生适应。在与不同的用户群体互动时，机器人应学会协助/指导他们执行复杂的认知任务，例如玩协作/竞争游戏进行认知训练，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 拨款适应性机器人情商福祉 (ARoEQ) 保持一致，旨在开发一个用于情感理解和机器人行为建模的整体自主系统，试图移动 远离绿野仙踪的方法。为伴侣机器人配备这种情感理解能力将使它们能够利用情感交互能力让用户参与认知任务。受情感计算核心原理的启发，该博士项目将（i）弥合依赖于特征的计算模型与对人类因素更深入的心理和认知理解之间的差距，以及（ii）建立一个整体模型，以实现机器人的情感行为以协助人类。

项目成果

Continual Learning for Affective Computing

• 发表时间: 2020-06
• 期刊: ArXiv
• 作者: Nikhil Churamani

Affect-Driven Learning of Robot Behaviour for Collaborative Human-Robot Interactions.

• DOI: 10.3389/frobt.2022.717193
• 发表时间: 2022
• 期刊: Frontiers in robotics and AI
• 影响因子: 3.4
• 作者: Churamani N;Barros P;Gunes H;Wermter S
• 通讯作者: Wermter S

Continual Learning for Affective Robotics: A Proof of Concept for Wellbeing

情感机器人的持续学习：幸福概念的证明

• DOI: 10.1109/aciiw57231.2022.10086005
• 发表时间: 2022
• 作者: Churamani N

Teleoperated Robot Coaching for Mindfulness Training: A Longitudinal Study

• DOI: 10.1109/ro-man50785.2021.9515371
• 发表时间: 2021-08
• 期刊: 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)
• 作者: I. Bodala;Nikhil Churamani;H. Gunes

Participant Perceptions of a Robotic Coach Conducting Positive Psychology Exercises: A Systematic Analysis

参与者对进行积极心理学练习的机器人教练的看法：系统分析

• DOI: 10.48550/arxiv.2209.03827
• 发表时间: 2022
• 作者: Axelsson M