In co-worker scenarios, flexibility by allowing for changes in kinematic configuration, e.g. through application of new tools or new degrees of freedom in re-configurable robots, is highly desirable. Current industrial practice requires costly and tedious reprogramming by experts. To facilitate and speed up this inefficient process, we propose to use a model-free learning method that enables a non-expert user to record a limited number of data-points in task-relevant areas of the workspace. So far, we have derived a benchmark scenario including reconfigurable physical obstacles and 3D-sensing capabilities and are planning a first extensive user study with an industrial partner.