  Multi-task learning (MTL) seeks to improve the generalized performance of
learning specific tasks, exploiting useful information incorporated in related
tasks. As a promising area, this paper studies an MTL-based control approach
considering Signal Temporal Logic (STL). Task compliance is measured via the
Robustness Degree (RD) which is computed by using the STL semantics. A suitable
methodology is provided to solve the learning and testing stages, with an
appropriate treatment of the non-convex terms in the quadratic objective
function and using Sequential Convex Programming based on trust region update.
In the learning stage, an ensemble of tasks is generated from deterministic
goals to obtain a strong initializer for the testing stage, where related tasks
are solved with a larger impact of perturbation. The methodology demonstrates
to be robust in two dynamical systems showing results that meet the task
specifications in a few shots for the testing stage, even for highly perturbed
tasks.
