Cognitive Architectures in Autonomous Robotics: A Systematic Review of Behavior Generation Approaches and Evaluation Strategies
Miguel Á. González-Santamarta, Francisco J. Rodríguez-Lera, Vicente Matellán Olivera · IEEE Access · 2025
Cognitive architectures are an essential component in the development of intelligent autonomous robots, enabling planning, learning, and adaptation to dynamic environments. This paper presents a systematic review of the literature focused on cognitive architectures applied to behavior generation in autonomous robotics. A structured search and selection process was conducted using multiple scientific databases, applying explicit inclusion, exclusion, and quality assessment criteria. From an initial set of 502 studies, 22 publications (2018–mid-2025) were selected for in-depth analysis. The review identifies a growing trend toward hybrid architectures that integrate symbolic reasoning with data-driven methods, such as neural networks and behavior trees. The types of architectures, tools used (such as symbolic planners, neural networks, and behavior trees), middleware employed (with a predominance of Robot Operating System), as well as the methods and metrics used in their evaluation, are analyzed. This review provides a detailed overview of the state of the art and highlights the technologies and approaches currently most widely used in the design of cognitive robotic systems.