The paper proposes a vision-based system for adaptively inferring the interactional intention of a person coming close to a robot, which plays an important role in the succeeding stage of human/robot cooperative handling of works/tools in production lines. Here, interactional intention is ranged in the meaning of the intention to interact/operate with the robot, which is proposed to be estimated by the human head moving path during an incipient period of time. To implement this intention inference capability, first, human entrance is detected and is modeled by an ellipse to supply information about the head position. Second, B-spline technique is used to approximate the trajectory with reduced control points in order that the system acquires information about the human motion direction and the curvature of the motion trajectory. Finally, Hidden Markov Models (HMMs) are applied as the adaptive inference engines at the stage of inferring the human interactional intention. The HMM algorithm with a stochastic pattern matching capability is extended to supply whether or not a person has an intention toward the robot at the incipient time. The reestimation process here models the motion behavior of an human worker when he has or doesn't have the intention to operate the robot. Experimental results demonstrate the adaptability of the inference system using the extended HMM algorithm for filtering out motion deviation over the trajectory.