As the demand for close cooperation between human and robots grows, robot manufacturers develop new lightweight robots, which allow for direct human-robot interaction without endangering the human worker. However, enabling direct and intuitive interaction between robots and human workers is still challenging in many aspects, due to the nondeterministic nature of human behavior. This work focuses on the main problems of interactive object transfer between a human worker and an industrial robot: the recognition of the object with partial occlusion by barriers including the hand to the human worker, the evaluation of object grasping affordance, and coping with inaccessible grasping points. The proposed visual servoing system integrates different vision modules where each module encapsulates a number of visual algorithms responsible for visual servoing control in humanrobot collaboration. The goal is to extract high-level information of a visual event from a dynamic scene for recognition and manipulation. The system consists of several modules as sensor fusion, calibration, visualization, pose estimation, object tracking, classification, grasping planning and feedback processing. The general architecture and main approaches are presented as well as the future developments planned.
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