Towards Privacy-Aware and Personalised Assistive Robots: A User-Centred Approach

TL;DR

This paper explores privacy-aware, user-centered assistive robots using federated learning to enhance personalization and data privacy, aiming to improve elderly care and user independence.

Contribution

It introduces federated learning into assistive robotics to address privacy concerns and improve personalization in elderly care applications.

Findings

Federated Learning enables privacy-preserving data sharing.

Enhanced personalization improves user independence.

Addresses challenges of non-stationary data in robotics.

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.