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Recently, several works achieve end-to-end visual servoing (VS) for roboticmanipulation by replacing traditional controller with differentiable neuralnetworks, but lose the ability to servo arbitrary desired poses. This letterproposes a differentiable architecture for arbitrary pose servoing: ahyper-network based neural controller (HPN-NC). To achieve this, HPN-NCconsists of a hyper net and a low-level controller, where the hyper net learnsto generate the parameters of the low-level controller and the controller usesthe 2D keypoints error for control like traditional image-based visual servoing(IBVS). HPN-NC can complete 6 degree of freedom visual servoing with largeinitial offset. Taking advantage of the fully differentiable nature of HPN-NC,we provide a three-stage training procedure to servo real world objects. Withself-supervised end-to-end training, the performance of the integrated modelcan be further improved in unseen scenes and the amount of manual annotationscan be significantly reduced.

A Hyper-network Based End-to-end Visual Servoing with Arbitrary Desired Poses