Towards Privacy-Aware and Personalised Assistive Robots: A User-Centred Approach
Fernando E. Casado
TL;DR
Privacy concerns limit data-driven personalization in assistive robotics. The paper advocates Federated Learning and continual FL to enable privacy-preserving, adaptive robot assistance in real-world settings. Key contributions include a formal CFL framework, FL for smart wheelchair navigation with shared control, and a taxonomy for handling heterogeneous data with personalization strategies. It also outlines future work on online FL with dynamic clustering, privacy-aware user interfaces, and HRI studies on privacy perception and trust. Overall, the work aims to enable responsible, privacy-aware integration of assistive robots to improve independence and quality of life for elderly and care-dependent individuals.
Abstract
The global increase in the elderly population necessitates innovative long-term care solutions to improve the quality of life for vulnerable individuals while reducing caregiver burdens. Assistive robots, leveraging advancements in Machine Learning, offer promising personalised support. However, their integration into daily life raises significant privacy concerns. Widely used frameworks like the Robot Operating System (ROS) historically lack inherent privacy mechanisms, complicating data-driven approaches in robotics. This research pioneers user-centric, privacy-aware technologies such as Federated Learning (FL) to advance assistive robotics. FL enables collaborative learning without sharing sensitive data, addressing privacy and scalability issues. This work includes developing solutions for smart wheelchair assistance, enhancing user independence and well-being. By tackling challenges related to non-stationary data and heterogeneous environments, the research aims to improve personalisation and user experience. Ultimately, it seeks to lead the responsible integration of assistive robots into society, enhancing the quality of life for elderly and care-dependent individuals.