  This work proposes a fast deployment pipeline for visually-servoed robots
which does not assume anything about either the robot - e.g. sizes, colour or
the presence of markers - or the deployment environment. In this, accurate
estimation of robot orientation is crucial for successful navigation in complex
environments; manual labelling of angular values is, though, time-consuming and
possibly hard to perform. For this reason, we propose a weakly supervised
pipeline that can produce a vast amount of data in a small amount of time. We
evaluate our approach on a dataset of remote camera images captured in various
indoor environments demonstrating high tracking performances when integrated
into a fully-autonomous pipeline with a simple controller. With this, we then
analyse the data requirement of our approach, showing how it is possible to
deploy a new robot in a new environment in less than 30.00 min.
