When Robots Learn: A Bibliometric Review of Artificial Intelligence in Engineering Applications of Robotics

The convergence of Robotics and artificial intelligence (AI) has transformed engineering by enabling the design of intelligent systems capable of learning, adapting, and performing complex tasks. These synergies are driving innovation across multiple engineering disciplines, including mechanical, materials, electrical, industrial, civil, and aerospace engineering. This review provides a comprehensive overview of the knowledge structure and emerging research directions of Robotics and AI in engineering, with the aim of identifying research trends, influential authors, leading institutions, and emerging thematic areas. Data were collected from the Web of Science and Scopus databases, covering the period from 2020 to 2025, and analyzed using bibliometric mapping techniques and performance indicators. The results reveal a sustained growth in research on autonomous systems, collaborative robots, and human–robot interaction within engineering contexts, with a strong emphasis on AI-driven optimization. Bibliometric analyses show that deep learning, reinforcement learning, and computer vision constitute the core enabling technologies structuring the field. In addition, the results highlight a high degree of international collaboration and a concentration of scientific output and impact in a limited number of leading countries, institutions, and journals.