  Constraint-based control approaches offer a flexible way to specify robotic
manipulation tasks and execute them on robots with many degrees of freedom.
However, the specification of task constraints and their associated priorities
usually requires a human-expert and often leads to tailor-made solutions for
specific situations. This paper presents our recent efforts to automatically
derive task constraints for a constraint-based robot controller from data and
adapt them with respect to previously unseen situations (contexts). We use a
programming-by-demonstration approach to generate training data in multiple
variations (context changes) of a given task. From this data we learn a
probabilistic model that maps context variables to task constraints and their
respective soft task priorities. We evaluate our approach with 3 different
dual-arm manipulation tasks on an industrial robot and show that it performs
better in terms of reproduction accuracy than constraint-based controllers with
manually specified constraints.
