Affective Mechanisms for Modelling Lifelong Human-Robot Relationships

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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.