Own and advance a SimPy-based discrete-event simulator for warehouse robotics, improving its realism, reliability, testing, and experiment infrastructure. Compare simulations with real operational data, tune parameters, reduce the sim-to-real gap, and develop scalable Gymnasium-style reinforcement learning environments. Train and evaluate learning-based policies, assess transfer to real-world fleets, productionize ML components, and communicate findings to technical and business stakeholders.