Description

One of the key components of introducing assistive robots in real environments is personalization. In assistive tasks, only the care recipient's preferences are typically included in the robot's decision-making, neglecting the caregiver's preferences and expertise. The goal of this thesis is to develop a robotic framework that can personalize interactions and tasks based on the preferences of both the care recipient and the caregiver. It will also actively learn from user feedback to adapt to preference changes.

The work is under the scope of the following projects:

• CHLOE-GRAPH: ClotH-Like ObjEcts Grasping, Representation, and Action Planning (web)
• ROB-IN: Robots para la asistencia continua y personalizada capaces de explicarse a sí mismos (web)
• FRAILWATCH: Sistema de Monitorització Robòtic de la Fragilitat per a Persones Grans (web)