After decades of development, microrobots have exhibited great application potential in the biomedical field, such as minimally invasive surgery, drug delivery, and bio‐sensing. Compared with conventional medical robotic systems, microrobots may be capable of reaching more narrow and vulnerable regions in the human body with minimal damage. However, limited by the small scale of microrobots, microprocessors, power supplies, and sensors can hardly be integrated on‐board. Thus, new strategies for the actuation and feedback for microrobots need to be explored. Furthermore, the open‐loop control method accomplished by operators may lack accuracy, and long‐duration operation could bring a severe physical challenge in many applications. Consequently, the automatic control of microrobots with the aid of control theories is developed to improve the control efficiency and precision. To further promote the automation level of microrobots, machine learning algorithms are expected to provide a solution to let microrobots adapt to more dynamic environments and undertake more complex medical tasks. Herein, a systematic introduction of the manipulation of microrobots from open‐loop to closed‐loop control is given in this review. It is envisioned that microrobots will play an important role in future biomedical applications.

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