Industrial robot process simulation plays an important role in production planning, optimization, and digital twin development. However, many existing simulation approaches rely on robot-specific and manually parametrized models, which limits scalability in heterogeneous robot environments. This paper proposes a unified machine learning–based simulation framework for modelling and simulating process behaviour across different industrial robot platforms using a shared task-space learning policy. The proposed approach combines reinforcement learning and inverse kinematics with clearly separated roles. The reinforcement learning policy generates end-effector motion in task space, while inverse kinematics converts the desired motion into robot-specific joint configurations and supports geometric feasibility. This separation reduces the dependence of the learning model on the joint structure of a particular robot. Experimental evaluation on heterogeneous robot platforms showed that the proposed framework can learn accurate target-reaching behaviour in simulation. The best-performing policy achieved a success rate close to 1.0, a mean final distance error of 0.0082 m, and a mean final orientation error of 5.55°. These results indicate that a shared task-space learning policy combined with robot-specific inverse kinematics can support scalable robot process simulation.