  Most artificial neural networks used for object detection and recognition are
trained in a fully supervised setup. This is not only very resource consuming
as it requires large data sets of labeled examples but also very different from
how humans learn. We introduce a setup in which an artificial agent first
learns in a simulated world through self-supervised exploration. Following
this, the representations learned through interaction with the world can be
used to associate semantic concepts such as different types of doors. To do
this, we use a method we call fast concept mapping which uses correlated firing
patterns of neurons to define and detect semantic concepts. This association
works instantaneous with very few labeled examples, similar to what we observe
in humans in a phenomenon called fast mapping. Strikingly, this method already
identifies objects with as little as one labeled example which highlights the
quality of the encoding learned self-supervised through embodiment using
curiosity-driven exploration. It therefor presents a feasible strategy for
learning concepts without much supervision and shows that through pure
interaction with the world meaningful representations of an environment can be
learned.
