  We introduce an approach to building a custom model from ready-made
self-supervised models via their associating instead of training and
fine-tuning. We demonstrate it with an example of a humanoid robot looking at
the mirror and learning to detect the 3D pose of its own body from the image it
perceives. To build our model, we first obtain features from the visual input
and the postures of the robot's body via models prepared before the robot's
operation. Then, we map their corresponding latent spaces by a sample-efficient
robot's self-exploration at the mirror. In this way, the robot builds the
solicited 3D pose detector, which quality is immediately perfect on the
acquired samples instead of obtaining the quality gradually. The mapping, which
employs associating the pairs of feature vectors, is then implemented in the
same way as the key-value mechanism of the famous transformer models. Finally,
deploying our model for imitation to a simulated robot allows us to study, tune
up, and systematically evaluate its hyperparameters without the involvement of
the human counterpart, advancing our previous research.
