A new “grasping by caging” solution by using eigen-shapes and space mapping

“Grasping by caging” has been considered as a powerful tool to deal with uncertainty. In this paper, we continue to explore into “grasping by caging” and propose a new solution by using eigen-shapes and space mapping. For one thing, eigen-shapes fix dexterous hands into a series of finger formations and help to reduce dimensionality and computational complexity. For the other, space mapping builds a mapping between rasterized grids in 2-D Work space (W space) and rasterized voxels in 3-D Configuration space (C space) and helps to rapidly reconstruct C space so that we can efficiently measure the robustness of caging and find an optimal caging configuration for grasping. Our algorithm can work rapidly and squeezingly cage any 2-D shapes, including objects with either convex boundaries, concave boundaries, 1-order or high-order boundaries and even objects with inner holes. We implement the algorithm with MATLAB and carry out experiments with WEBOTS simulation to test its robustness to uncertainties. The results show that our algorithm can work well with various object shapes and can be robust to noisy control and noisy perception. It is promising in the power grasping tasks of dexterous hands.