Topic: Perception, Collaboration, and Autonomous Execution of Intelligent Robots in Medical Environments

The integration of intelligent robots into medical environments has the potential to transform healthcare delivery by enhancing precision, safety, and efficiency. Recent advancements in artificial intelligence (AI), sensor technologies, and robotics have enabled robots to perceive complex and dynamic medical scenarios, collaborate effectively with human professionals, and autonomously perform a wide range of tasks, from surgical assistance to patient monitoring.

Despite this progress, significant challenges remain. Developing robust perception systems, achieving seamless human-robot collaboration, and ensuring reliable autonomous operation in sensitive and dynamic healthcare settings are critical research frontiers.

This Special Issue aims to showcase cutting-edge research that addresses these challenges and explores innovative solutions for intelligent robotic systems in medical contexts. We invite high-quality original research articles and comprehensive review papers on topics related to robotic perception, human-robot collaboration, and autonomous execution in healthcare and medical environments.

Topics of interest include, but are not limited to:
● Multimodal sensing in medical environments;
● Computer vision for surgical and clinical tasks;
● Sensor fusion and real-time perception;
● Human-robot interaction in clinical workflows;
● Teaming and coordination with medical staff;
● Cognitive and social modeling for collaborative robotics;
● Task planning and autonomous decision-making;
● Navigation and manipulation in complex healthcare settings;
● Safety, reliability, and risk-aware autonomous operations;
● Robotic surgery and rehabilitation;
● Telemedicine and remote patient care;
● Hospital logistics, delivery, and disinfection robots;
● AI-driven learning for adaptive behaviors;
● Simulation and digital twins for medical robotics.

Medical robotics, medical embodied intelligence, multimodal perception, computer vision, human-robot interaction, autonomous decision-making, adaptive learning, digital twins