The available knowledge of insect flight seems to suggest that two-winged flies implement a feedback control paradigm not exploited in engineering and have remarkable performance, achieved in a computationally simple way. This sensor-rich feedback control paradigm relies on extensive, distributed measurement of the quantities of interest in space and time. The visual system of the fly represents its motion relative to the environment through coarse, but still dense, representation of the vector field involved instead of detailed imaging by fine pixels. This representation is global, covers the whole of space, and involves several overlapping patches. The lengths of the vectors in the patches depend on the insect's direction of motion. Hence, the complex underlying differential equations of motion need not be integrated numerically, as their solutions are readily available through interpolation of the detailed, global representation of the relevant vector field. Also, this redundant representation may allow fine control of high agility maneuvers, despite inertial couplings, with little computation. Indeed, a series of small adjustments should suffice for the desired vector field patterns to be achieved. From the engineering viewpoint, this opens not only new perspectives in control, but also in sensors and instrumentation. It suggests that there is no need for high-resolution imaging cameras for flight control based on optic flow. Instead, coarse-grained arrays of sensors could give good results provided they are: arranged to offer a global view of the environment; endowed with parallel processing for extraction of the global vector field of motion via several, overlapping patches of the field; characterized by sensitivities dependent on the direction of motion. The use of other sensors is likely to be needed, and this will call for multisensor data fusion. However, the sensor-rich feedback control architecture works on the complementarity of the type of information measured, not the type of instrument used. A gravity sensor or an altimeter, for example, can replace ocelli. So, the issue is not to emulate insect sensors, but to use appropriate instrumentation to extract the relevant information with the required speed and accuracy. Further, de- tailed and multidisciplinary research is needed to reverse engineer insect flight control completely. However, what we know already hints of exciting possibilities of understanding motion control in animals and humans and acquiring a new paradigm in control engineering. The latter will have a large impact not only on the vast applications of automatic control, but also on the sensor, instrumentation, and measurement communities.