Prior robot-assisted cranium-milling studies only considered controlling the force in the skull’s vertical direction and neglected the milling cutter’s feed force. Additionally, achieving stable force control in multiple directions is challenging for robots due to the uneven skull surface. Here a hybrid admittance control algorithm incorporating a model-free adaptive nonlinear force control and fuzzy control algorithms is proposed to accomplish effective automatic cranial-milling tasks. First, a pure data-driven model-free adaptive control method based on partial form dynamic linearization is used to control the feed force. Second, fuzzy control minimizes the total error of both the vertical and feed force by adaptively adjusting the milling cutter’s velocity and position. 42 ex vivo animal skull-milling experiments conducted by the automatic robotic cranium-milling system indicate that when using the proposed control algorithm, the force error percentage can be maintained below 5.0% within 3 s and the maximal root mean square error percentages for vertical and feed force are 1.85% and 1.94%, respectively. Moreover, no instances of dura mater damage are observed and the robotic system exhibits a high level of autonomy as it performs the skull milling task with minimal human involvement throughout the entire experiment. The results suggest the potential for advancing the intelligence level of neurosurgery in the future.