This brief paper investigates the control of a robotic bulldozing operation. Optimal blade position control laws were designed based on a hybrid dynamic model to maximize the predicted material removal rate of the bulldozing process. Experiments were conducted with a scaled-down robotic bulldozing system. The control laws were implemented with various tuning values. As a comparison, a rule-based blade control algorithm was also designed and implemented. The experimental results with the best optimal controller demonstrated a 33% increase in the average material removal rate compared to the rule-based controller.