The development of haircutting robots has gained significant attention for applications in personal grooming and healthcare, driven by population aging, labor shortages, and increased demand for contactless interaction. Existing efforts, however, remain fragmented, addressing perception, hair modeling, or motion planning in isolation without a systematic framework. This survey provides a comprehensive review of robotic haircutting through the lens of Computer Numerical Control (CNC) machining principles, proposing a unified framework that treats hair manipulation with industrial manufacturing rigor. We systematically analyze the field across five key dimensions: system architectures including gantry-based, manipulator-based, and hybrid configurations; sensing modalities encompassing vision, force, proximity, and tactile feedback; hair modeling techniques from physics-based simulation to neural reconstruction methods; toolpath planning strategies adapted from CNC machining including coverage planning and multi-pass optimization; and control approaches for precision execution and safety assurance. We present detailed comparisons of existing prototypes and commercial systems, identify key technical challenges including head motion compensation, hair property variability, and safety-critical control, and outline promising research directions. This survey bridges the gap between service robotics and precision manufacturing, providing researchers with a structured foundation for advancing haircutting robot technology toward practical deployment.
Li et al. (Thu,) studied this question.