Robotic systems operating in unpredictable real-world scenarios require high levels of intelligence and adaptability. This thesis presents the PACT (Predictive Adaptive Collaborative Twin) framework, which tightly integrates a real robotic system with its Digital Twin, enabling a unified workflow for training, validation, and adaptive improvement, with reinforcement learning (RL) as the core source of intelligence. The framework’s practical applicability is demonstrated on a simplified real-world grasping task, confirming PACT’s ability to bring training, deployment, and adaptation together holistically, while also identifying areas for future improvement.