## 1 Introduction  
Human collaboration with robots in a coworking space or working with robots in a partnership manner is described as a close-proximity human-robot interaction (HRI) [1; 2; 3; 4]. This collaboration between human operators and robots becomes possible in production systems mainly due to the improvements in safety design and autonomy of robots [5; 6; 7; 8; 9]. The combination of performative accuracy of collaborative robots with the planning flexibility of human actors has resulted in novel production applications that allow more natural and effective interaction between humans and robots with the objective of smart production [3; 10; 11; 12; 13]. The immediate collaboration between human operators and robots becomes possible in production systems mainly due to the improvements in safety design [14].  
In smart production, the handover is one of the basic interactive elements in the context of close-proximity HRI. A significant amount of research has been conducted in recent years to explore the different facets of handovers [15], including grasp path planning [16; 17], grasp power control during a handover process [18], fluency [19], and social interactivity [20; 21; 22].  
This study investigated the context in which a robot must hand over a part to a human partner to complete an assembly task in a close-proximity human-robot collaborative environment. For the handover process to be completed smoothly, the robot must estimate the human worker's task completion time and be able to self-adjust the cycle time between different skill levels of assembly workers. Prediction and timeliness on the side of the robot can affect performance and preference of handover tasks between humans and robots [23]. When humans are performing an assembly task, the causes of the variation in the assembly time may be divided into two categories. The first category is causable and trending variation due to the learning curve or undesirable internal or external factors (e.g., fatigue, noise, ambient temperature, and vibration). For instance, progressing tiredness of the human operator leads to lower processing speed and considerably varying processing time. The second category is noncausable randomness. In this case, assembly time variation is simply caused by nonattributable random errors (e.g., accidentally dropping an assembly part, sudden itch as a trigger to scratch a body part, etc.). The present study used the common building blocks assembly task [24,25] as a collaborative task between robots and operators to explore the following two work scenarios: (1) a general repetitive assembly task during which mainly only nonattributable random errors occur in the cycle time; and (2) repetitive assembly in which the human operator exhibits a learning curve, resulting in causable and trending cycle time variation. The method-time measurement (MTM), Kalman filter [26], and trigger sensor predictive models were employed to compare the cycle time of the assembly task, waiting times of robot and operator, and the participants' subjective preferences. The objective was to obtain a predictive handover model with lower waiting times, shorter cycle time, and higher efficiency.