Personalized Companion Robot for Open-Domain Dialogue in Long-Term Elderly Care

With more than 1 billion people over 60 worldwide, creating technology that supports the aged to live independently for longer by assisting them in everyday tasks became essential. While companion robots are aimed toward this need, current technology falls short in maintaining engagement over long-term interactions. Among the reasons is the inability to learn from users and adapt, known as lifelong learning, especially in open-domain dialogue that is not limited to any topic.

The project aims to develop a long‐term memory model for open‐domain dialogue such that a robot can learn and recall a person’s attributes, preferences, and shared history to provide personalized assistance in a variety of tasks, such as performing preferred activities, adaptive collaboration in chores, and providing reminders based on their schedule and needs.

Researchers

Post-doctoral Fellow

Bahar Irfan is a Postdoctoral researcher at TMH - KTH Digital Futures. Her research focuses on creating personal robots that can continually learn and adapt to assist everyday life. Previously, she was a Research and Development Associate at Evinoks Service Equipment Industry and Commerce Inc., developing customizable software for industrial robots and smart buffets. Before that, she worked as an R&D Lab Associate at Disney Research Los Angeles on emotional language adaptation in multiparty interactions.

She has a diverse background in robotics, from personalization in long-term human-robot interaction during her PhD at the University of Plymouth and SoftBank Robotics Europe as a Marie Skłodowska-Curie Actions fellow to user-centred task planning for household robotics during her MSc in computer engineering, and building robots for BSc in mechanical engineering at Boğaziçi University.

Supervisors

Duration

June 2022 -- June 2024

Funding

This project is funded by Digital Futures .