  Very distinct strategies can be deployed to recognize and characterize an
unknown environment or a shape. A recent and promising approach, especially in
robotics, is to reduce the complexity of the exploratory units to a minimum.
Here, we show that this frugal strategy can be taken to the extreme by
exploiting the power of statistical geometry and introducing new invariant
features. We show that an elementary robot devoid of any orientation or
observation system, exploring randomly, can access global information about an
environment such as the values of the explored area and perimeter. The explored
shapes are of arbitrary geometry and may even non-connected. From a dictionary,
this most simple robot can thus identify various shapes such as famous
monuments and even read a text.
