Collaborative robots (cobots) are transforming critical infrastructure sectors, from electricity production to transportation, emphasizing the crucial need for human–robot interaction with user-friendly interfaces, safety measures, and training programs. With the assistance of artificial intelligence, cobots adapt to human preferences and enhance their cooperation capabilities, showcasing their transformative impact on infrastructure security and efficiency. Meanwhile, achieving autonomy in robots remains a major challenge, categorized into human–robot collaborations based on various criteria aimed at improving necessary domains. The robot undergoes four standard stages: perception, decision-making, motion execution, and evaluation. This paper focuses on optimizing collaboration by enhancing the robot's decision-making process, assessed through various performance metrics. The developed framework enhances human–robot collaboration by considering diverse metrics, independently of human behavior.