Robust grasp preimages under unknown mass and friction distributions

This work introduces an algorithm for the computation of robust grasp preimages: the space of initial poses from which an object will converge into the desired grasp. Building on existing motion and friction models for pushed objects under contact, we describe a game-theoretic technique for estimating worst-case scenarios for difficult to observe properties like pressure and friction distributions. The use of this antagonistic model in the grasping simulations provides for a conservative estimate of the preimage of the given grasp. The antagonistic model is then validated against data from real grasping experiments on various robot grippers.