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.

利用视觉伺服任务作为图遍历，通过分割和重组现有的演示数据构建演示图，并利用相似性函数选择最佳演示，从而实现学习的操作策略具有较强的任务传递能力和多任务能力。