This study aims to realize a precision assembly robot system. This system focuses on the mating of precision parts, which is difficult with the current technology, and uses workpiece movements and the sensation of human fingertips as a reference to eliminate assembly failures, such as shaft and hole biting. This approach solves the limitations in robotizable tasks, long teaching times, and possible assembly failures by robots. The proposed method involves measuring human task data, analyzing the movement of assembly parts using averaging (a simple feature extraction method), and deriving the corresponding robot movements. Furthermore, a system is developed to predict decisive assembly failures from force information obtained during tasks by analyzing the force sensation of the human fingertips using a support vector machine (a type of machine learning). Equipped with the prediction system and the derived workpiece motion, the robot performs assembly tasks that typically require human skills.

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