## 1 Introduction  
As dominators of the earth, human beings have higher intelligence than any other biological species. Humans are skilled in summarizing rules from observed phenomena, forming knowledge, and applying it to understanding new things. Throughout history, the evolution of humans is basically the process by which new methods and techniques are created to alleviate human labor, thus promoting the development of science and technology. As one of the greatest inventions in the 20 th century, robotics has made great progress since the late 1950s. The emergence of robots is the inevitable trend of social and economic development, and its rapid growth has been improving the level of social production and the quality of human life. Although people are eager to have robots in their homes, there is no doubt that the impact of robots on manufacturing is even greater, especially in the last decade when traditional manufacturing has undergone unprecedented changes. Due to the yearing for a better life, many people are unwilling to choose dangerous or boring jobs, which makes different types of human jobs face labor shortages. In this context, industrial robots show extraordinary ability in replacing human labor and improving productivity. However, some tasks that rely heavily on human dexterity or intelligence remain difficult to replace by robots. On one hand, while most industrial robots are adept at repetitive tasks, they are not flexible enough to manipulate forces, especially for some precision or fine machining; on the other hand, most robots today and fully automated production lines are not very good at understanding human intentions or learning sequential strategies from human behavior. Therefore, these automated devices are very sensitive to changes in context. Tosolve these problems, intelligent learning algorithms and human demonstrations aimed at improving robot autonomy have been studied [1; 2]. In this work, we mainly consider the second aspect mentioned above. The tuning process of microwave cavity filter is studied in detail. Tuning is the last step of the cavity filter before it leaves the factory. It is usually a boring and complicated work, which is completed manually by experienced tuning technicians. Our goal is to automate tuning. The cavity filter is a metal device for filtering signals and suppressing noises, which is often used in microwave, satellite, radar, electronic countermeasure (ECM), and various electronic testing devices. A cavity filter is mainly composed of cavities with resonators, inserted tuning screws, locking screw-nuts, and a covering plate; see Figure 1a. A cavity filter is a product that passes or eliminates microwave signals in specific frequency bands. Due to high working frequencies and inevitable manufacturing errors, unfortunately, almost every cavity filter product needs to be manually adjusted to meet its design specifications. During the tuning process, the measured cavity filter is connected to a Vector Network Analyzer (VNA) (Figure 1b) and iteratively adjusted by a technician with a screwdriver. Almost every inserted screw will be tuned according to the S-parameter curves shown in the VNA. The curves indicate the current tuning state of the product and give guidance to the tuning technician to perform the next action such that the curves are gradually optimized until reaching the desired targets. Figure 2 shows the overall manual tuning process. There are numerous possible difficulties during the task of cavity filter tuning. First, the relations between tuning screws and S-parameters would be complex. Theoretical analysis does not ensure validity since the manufacturing errors are not considered. In fact, each screw has its physical function in adjusting the S-parameter curves, but the actual product differs a lot from the designed pattern.