DEEP REINFORCEMENT LEARNING IN HEALTHCARE ROBOTICS: A PATHWAY TOWARDS INTELLIGENT ASSISTIVE CARE

Abstract

Healthcare robotics is rapidly evolving to address the challenges of an aging population, rising medical costs, and the need for personalized care. Among the most transformative technologies is deep reinforcement learning (DRL), which enables robotic systems to adapt, learn, and optimize complex tasks autonomously. This article critically examines the role of DRL in healthcare robotics with a focus on intelligent assistive care. DRL empowers robotic systems to perform motor control, navigation, human-robot interaction, and adaptive decision-making in uncertain clinical environments. The study highlights DRL’s integration into robotic rehabilitation, surgical assistance, elderly care, and patient monitoring. Furthermore, a comparative analysis is presented to evaluate DRL’s superiority over conventional control strategies in terms of adaptability, safety, and personalization. Graphical representations illustrate the growth trajectory of DRL research in healthcare robotics, its clinical adoption rates, and potential cost-reduction outcomes. A tabular summary outlines state-of-the-art DRL frameworks and their applications in healthcare robotics. The discussion explores empirical insights, challenges, ethical considerations, and future directions, emphasizing the potential of DRL to establish a new paradigm in healthcare delivery through intelligent, safe, and empathetic robotic care.