Towards dextrous manipulation using manipulation manifolds

In dextrous manipulation, 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 very restricted task specification and performing quite artificially looking movements. In this paper, we present the first steps towards a representation of manipulation movements recorded from human demonstration which 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 the way of applying it for manipulation in the example of turning a bottle cap in a physics-based simulation.