Towards Privacy-Aware and Personalised Assistive Robots: A User-Centred Approach
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 23 May 2024.
Builds on
9 of 27 references.
References · 9
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 2,993
- 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
- 2015Characterizing Concept Driftcited by 93
- 2019Energy Demand Prediction with Federated Learning for Electric Vehicle Networkscited by 46
- 2021Federated Learning for Malware Detection in IoT Devicescited by 36
- 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
- 2021Concept drift detection and adaptation for federated and continual learningcited by 32
- 2022Federated Continual Learning for Socially Aware Roboticscited by 5
Built on by
Not cited yet.