The growing need for sustainable and flexible automation solutions has led to the exploration of transforming traditional industrial robots into collaborative robots (cobots). This paper presents a framework for the conversion of conventional industrial robots into safe, intelligent, and sustainable cobots, leveraging advancements in artificial intelligence and computer vision and the principles of the circular economy. The proposed modular framework contains key components such as visual perception, cognitive adaptability, safe human–robot interactions, and reinforcement learning-based decision-making. Our methodology includes a comprehensive analysis of safety standards (e.g., ISO/TS 15066), robot typologies suitable for retrofitting, and sustainability strategies, including remanufacturing and lifecycle extension. A multi-phase implementation approach is laid out for a theoretical design to contribute to the development of cost-effective and environmentally responsible robotic systems, offering a scalable solution for extending the usability and social acceptance of legacy robotic platforms in collaborative settings.

Extended reality (XR) technologies are gaining traction in technical education due to their potential for creating immersive and interactive training environments. This study presents the development and empirical evaluation of X-RAPT, a collaborative VR-based platform designed to train students in industrial robotics programming. The system enables multi-user interaction, cross-platform compatibility (VR and PC), and real-time data logging through a modular simulation framework. A pilot evaluation was conducted in a vocational training institute with 15 students performing progressively complex tasks in alternating roles using both VR and PC interfaces. Performance metrics were captured automatically from system logs, while post-task questionnaires assessed usability, comfort, and interaction quality. The findings indicate high user engagement and a distinct learning curve, evidenced by progressively shorter task completion times across levels of increasing complexity. Role-based differences were observed, with main users showing greater interaction frequency but both roles contributing meaningfully. Although hardware demands and institutional constraints limited the scale of the pilot, the findings support the platform’s potential for enhancing robotics education.