Benefits of pErsonalization and behAvioral adaptation in assistive Robots

With rapid technological advancements and pressing societal challenges—such as demographic shifts, a shortage of medical professionals, and increasing care demands—our future is undeniably heading toward a hybrid reality where humans and social robots coexist and collaborate across diverse domains. Particularly in healthcare and assistive contexts, social robots are being adopted to support vulnerable populations including older adults, individuals with chronic illnesses, and children with developmental disabilities. These robotic systems promise to augment human capabilities, enhance efficiency, and improve quality of life by offering personalised, continuous, and adaptive assistance tailored to individual needs, habits, and preferences.

However, the realisation of meaningful and effective human-robot interaction (HRI) requires a nuanced understanding of human behaviour, cultural dynamics, and context-sensitive requirements. Personalisation emerges as a crucial strategy in this pursuit, enabling robots to adapt socially and cognitively to the people they assist. Advances in Artificial Intelligence (AI), the Internet of Things (IoT), and robotics—particularly the integration of Large Language Models and cognitive architectures—are facilitating the design of socially intelligent robots capable of emotional communication, mutual affective understanding, and shared mental models that foster trust and long-term collaboration.

Yet, these benefits come with considerable risks. Personalisation may undermine user autonomy by fostering overdependence, diminishing self-sufficiency, or reducing opportunities for personal growth. It risks narrowing users’ worldviews through over-customisation that limits exposure to new ideas, behaviours, or possibilities. Furthermore, as personalisation systems increasingly rely on user data, ethical concerns around privacy, transparency, and trust become paramount. Misaligned or biased personalisation mechanisms could exclude or disadvantage certain groups, threatening inclusivity and equity in human-robot interactions. To realise the potential of a hybrid human-robot future, it is essential to strike a balance that optimises personalisation without compromising user autonomy, diversity, and privacy. Personalisation must respect user agency, encourage exploration beyond comfort zones, and foster equitable and inclusive interactions while safeguarding ethical principles. This requires carefully considering how personalisation is implemented and ensuring its benefits are maximised while potential risks are mitigated.

Contributing Workshops

List of Topics

• Personalisation in short and long-term HRI
• User modelling in HRI
• Robot's personality
• Context and situation awareness for robots
• Engagement evaluation and re-engagement strategies
• Personalised dialogue with robots
• Personalised non-verbal behaviour with robots
• Adaptive human-aware task planning
• Theory of Mind for adaptive interaction
• Machine Learning for robotic personalisation
• Lifelong (continual) learning for adaptation
• Adaptation in multimodal interaction
• Affective and emotion-adapted HRI
• Persuasion in HRI
• Culture-aware robots
• Evaluation metrics for adaptive robotic behaviour
• Ethical implications of personalisation
• Robot customisation and teaching