Using Manifolds for Dextrous Hand Control

In dextrous hand control, the implementation of manipulation movements still is a complex and intricate undertaking. Often, a lot of object physics and modelling effort has to be incorporated into a controller working only for a restricted task specification and performing quite artificially looking movements. In this paper, starting from our representation for dextrous grasping, the Grasp Manifold, we motivate and present adaptations which enable the new manifold – the Manipulation Manifold – to robustly represent manipulation movements which have been recorded from human demonstration. It facilitates later application and promotes natural motion. We use manifolds of hand postures embedded in the finger joint angle space which are constructed such that manipulation parameters including the advance in time are represented by distinct manifold dimensions. This allows for purposive navigation within such manifolds. We present the manifold construction using the Unsupervised Kernel Regression (UKR) and our first results of representing the turning movement of a bottle cap.