Towards Autonomous Verification: Integrating Cognitive AI and Semantic Digital Twins in Medical Robotics

In medical laboratory environments, where pre-cision and safety are critical, the deployment of autonomous robots requires not only accurate object manipulation but also the ability to verify task success to comply with regulatory requirements. This paper introduces a novel imagination-enabled perception framework that integrates cognitive AI with semantic digital twins to allow medical robots to sim-ulate task outcomes, compare them with real-world results, and autonomously verify the success of their actions. Our approach addresses challenges related to handling small and transparent objects commonly found in sterility testing kits and other related consumables. By enhancing the RoboKudo perception system with parthood-based reasoning, we enable more accurate task verification through focused attention on object subparts. Experiments show that our system significantly improves performance compared to traditional object-centric methods, increasing accuracy in complex environments without the need for extensive retraining. This work demonstrates a novel concept in making robotic systems more adaptable and reliable for critical tasks in medical laboratories.