  Learning-based manipulation policies from image inputs often show weak task
transfer capabilities. In contrast, visual servoing methods allow efficient
task transfer in high-precision scenarios while requiring only a few
demonstrations. In this work, we present a framework that formulates the visual
servoing task as graph traversal. Our method not only extends the robustness of
visual servoing, but also enables multitask capability based on a few
task-specific demonstrations. We construct demonstration graphs by splitting
existing demonstrations and recombining them. In order to traverse the
demonstration graph in the inference case, we utilize a similarity function
that helps select the best demonstration for a specific task. This enables us
to compute the shortest path through the graph. Ultimately, we show that
recombining demonstrations leads to higher task-respective success. We present
extensive simulation and real-world experimental results that demonstrate the
efficacy of our approach.
