AI-Driven Adaptive Autonomy: Is AI Really Pervasive? Research Gaps from Bibliometric Assessment

Abstract

Artificial Intelligence is widely recognised as a driver of adaptive autonomy in robotics. Yet, the extent to which AI techniques truly permeate the functional architecture of autonomous systems is still only partially characterised. Existing bibliometric analyses typically map research themes, keywords or algorithms but provide limited insight into how contributions distribute across the functional logic of autonomous systems. This raises a fundamental question: is AI really pervasive across the functions that enable robots to act adaptively in complex environments? Which areas are mature or under-explored? To achieve this outcome, the paper adopts a functional, control-loop-oriented perspective that avoids the bias of vertical domains or robot-specific applications, More than 2500 scientific works, published in the last 25 years, were mapped across the 13 functional modules, using a multi-label neural classification pipeline, and analysed via co-occurrence and structural techniques. This approach allowed to highlight not only areas where AI is already known to be central and consistently confirmed, but also those where its impact would be expected to be significant yet remains surprisingly limited. By combining architectural reasoning with bibliometric evidence, the study provides a broader lens for assessing research gaps and for situating current advances within the long-term agenda of adaptive and human-centred autonomy.