AI and Robotics in Tribological Experimentation: Robotic Platforms, Artificial Intelligence, and Closed-Loop Evaluation

Tribology, the science of friction, wear, and lubrication, governs the reliability of nearly every mechanical system. Tribological contacts account for approximately 23% of global energy consumption, including 20% used to overcome friction and 3% associated with remanufacturing worn components. Nevertheless, the field has remained constrained by low experimental throughput, operator variability, and a scarcity of standardized, reusable datasets. This review surveys two converging trends positioned to address these limitations: the development of robotic and automated platforms for tribological experimentation, and the growing application of artificial intelligence to tribological analysis. High-throughput tribometer architectures, robotic specimen preparation, and multi-modal in situ sensing are examined as components of an emerging automated tribometry infrastructure. Supervised learning, physics-informed neural networks, and Bayesian optimization are reviewed as AI methods organized by the data regime in which they operate. The convergence of these trends in closed-loop autonomous tribological experimentation is assessed, including system architecture, optimization target specification, current partial implementations, and tribology-specific integration barriers that distinguish this domain from adjacent self-driving laboratory applications. Application domains spanning industrial machinery, biomedical implants, and aerospace and automotive drivetrains are discussed. Key challenges including dataset standardization, model transferability, and hardware-software integration complexity are identified. Prospects for fully autonomous tribological discovery pipelines are outlined, with emphasis on open-access data infrastructure and physics-constrained learning as the enabling conditions for the field.