Vision based manipulation of non-rigid objects

Since the analytical expressions for the representation of nonrigid object structure and motion are severely underconstrained, current techniques for nonrigid object manipulation employ physical object models known prior to sensing. Recently, however, psychophysical studies have revealed that humans are able to discover proper motor coordination skills through sensory input without the use of previously known physical models. In this paper, a robust, discovery-driven, vision-based robotic manipulation algorithm for nonrigid objects, based on the novel concept of relative elasticity, is developed which requires the use of no a priori physical models. The manipulation technique is also experimentally verified on different flexible linear objects.