Autonomous development

This research stream focuses on defining and operationalizing autonomy levels in unmanned systems (UMS) to provide a common terminology, metrics, and frameworks that facilitate communication among practitioners, guide system development, and support autonomy evaluation. As autonomy spans multiple domains and involves human interaction, task complexity, and environmental variability, these works emphasize multidimensional characterization and consensus-based models useful across military, industrial, and rescue applications.

This theme investigates theoretical and computational models that describe and enable autonomous cognition, learning, and behavioral adaptation, framing autonomy as an emergent, layered property evolving from reflexive to cognitive levels. It explores biological inspirations, system intelligence hierarchies, and self-programming mechanisms, detailing how autonomous behavior and decision-making can be developed and formalized in artificial agents and robots. This body of work provides foundational understanding of autonomous systems’ intelligence structures and developmental pathways.

This research focus examines frameworks and architectures enabling robots to vary their autonomy levels dynamically, granting humans meta-level control over when and how autonomous functions execute. It centers on mixed-initiative interaction protocols and interfaces that negotiate workload, communication delays, and shared decision-making. By exploring adjustable autonomy, these studies address challenges of reliability, safety, and usability in multi-robot and multi-user environments, advancing practical deployment of semi-autonomous robotic systems.