Cognitive training for adults with developmental deficits led by a socially intelligent robot

Abstract

This work presents a socially intelligent robotic system for cognitive training of adults with developmental deficits in a therapist-half-supervised setting. The core idea is to move beyond one-way robot prompting toward a structured, ethically grounded, data-aware intervention framework in which the robot, end user, and therapist form a coordinated triad. The proposed system combines robot behaviour management, session orchestration, user monitoring, and therapist oversight within a unified architecture suitable for iterative real-world deployment.The benefit of this approach lies in the effective integration of artificial intelligence components, human-robot interaction, psychology, and the extensive experience of human experts. Interaction in a real-world environment and the labeling of test datasets occur simultaneously. The contribution is fourfold. First, we define a computer-robot-therapist-end-user architecture that explicitly models information flow, therapist control, and adaptive interaction. Second, we provide an experimental procedure for cognitive training sessions that includes training content, session organization, and a user-experience evaluation framework. Third, we introduce a data protection and ethics plan tailored to vulnerable populations, emphasizing informed participation, controlled data access, and responsible system adaptation. Fourth, we outline an approach for objective estimation of the end user’s cognitive state, with the longer-term goal of enabling robot behaviour that is responsive to fatigue, attention, overload, or engagement. Preliminary results indicate that socially guided robot-based training is feasible and acceptable within the proposed framework. A central ongoing challenge is to identify meaningful robot-user interaction events in the relation to their contexts (situations) and to map them to suitable user and robot actions. We therefore treat interaction-event modelling not as a secondary implementation detail but as a foundational design layer for adaptive socially assistive robotics. The longer-term significance of this work lies in connecting clinical and social needs with computational interaction design, making robot-led training more personalized, transparent, and operationally safe for adults with developmental deficits.