Integrating Reinforcement Learning and AI Agents for Adaptive Robotic Interaction and Assistance in Dementia Care
Journal:
arXiv
Published Date:
Jan 28, 2025
Abstract
This study explores a novel approach to advancing dementia care by
integrating socially assistive robotics, reinforcement learning (RL), large
language models (LLMs), and clinical domain expertise within a simulated
environment. This integration addresses the critical challenge of limited
experimental data in socially assistive robotics for dementia care, providing a
dynamic simulation environment that realistically models interactions between
persons living with dementia (PLWDs) and robotic caregivers. The proposed
framework introduces a probabilistic model to represent the cognitive and
emotional states of PLWDs, combined with an LLM-based behavior simulation to
emulate their responses. We further develop and train an adaptive RL system
enabling humanoid robots, such as Pepper, to deliver context-aware and
personalized interactions and assistance based on PLWDs' cognitive and
emotional states. The framework also generalizes to computer-based agents,
highlighting its versatility. Results demonstrate that the RL system, enhanced
by LLMs, effectively interprets and responds to the complex needs of PLWDs,
providing tailored caregiving strategies. This research contributes to
human-computer and human-robot interaction by offering a customizable AI-driven
caregiving platform, advancing understanding of dementia-related challenges,
and fostering collaborative innovation in assistive technologies. The proposed
approach has the potential to enhance the independence and quality of life for
PLWDs while alleviating caregiver burden, underscoring the transformative role
of interaction-focused AI systems in dementia care.
