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It is challenging to realize the autonomy of industrial robots under external and internal uncertainties. A majority of industrial robots are supposed to be programmed by teaching-playback method, which is not able to handle with uncertain working conditions. Although many studies have been conducted to improve the autonomy of industrial robots by utilizing external sensors with model-based approaches as well as adaptive approaches, it is still difficult to obtain good performance. In this chapter, we present a dynamic compensation framework based on a coarse-to-fine strategy to improve the autonomy of industrial robots while at the same time keeping good accuracy under many uncertainties. The proposed framework for industrial robot is designed along with a general intelligence architecture that is aiming to address the big issues such as smart manufacturing, industrial 4.0.