We present an online learning approach for developing sensor-based controllers and show how it can make the programming of assembly tasks easier. We suggest combining the qualitative modelling of human expert skills and the self-tuning of control parameters so that a controller for a complex assembly task can be efficiently developed. It is then discussed how to construct a fuzzy controller with B-splines and why rapid convergence of its learning can be achieved. To apply the concept in a screwing operation, we propose several gradual steps of active online learning. Experiments were carried out with two independently controlled robot arms. Although general-purpose jaw-grippers are used and diverse uncertainties exist, the "elevator control" of a toy aircraft can be robustly built.
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