Many animals, especially insects, compute and use optic flow to control their motion direction and to avoid obstacles. Recent advances in computer vision have shown that an adequate optic flow can be computed from image sequences. Therefore studying whether artificial systems, such as robots, can use optic flow for similar purposes is of particular interest. Experiments are reviewed that suggest the possible use of optic flow for the navigation of a robot moving in indoor and outdoor environments. The optic flow is used to detect and localise obstacles in indoor scenes, such as corridors, offices, and laboratories. These routines are based on the computation of a reduced optic flow. The robot is usually able to avoid large obstacles such as a chair or a person. The avoidance performances of the proposed algorithm critically depend on the optomotor reaction of the robot. The optic flow can be used to understand the ego-motion in outdoor scenes, that is, to obtain information on the absolute velocity of the moving vehicle and to detect the presence of other moving objects. A critical step is the correction of the optic flow for shocks and vibrations present during image acquisition. The results obtained suggest that optic flow can be successfully used by biological and artifical systems to control their navigation. Moreover, both systems require fast and accurate optomotor reactions and need to compensate for the instability of the viewed world.