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The Potential Grasp Robustness (PGR) metric considers different states of the contact points, relaxing the requirement of all points being far from the friction cone boundary. The addition of new states for each contact point increases the computational complexity, which is combinatorial on the number of states and takes a long time for grasping configurations with large number of hand-object contacts. In this work we analyse the computational complexity of two recently proposed heuristics, which consider that: (i) the minimum number of contact points needed could be in two states and (ii) an analysis of grasp contact data provides the most common combinations of contact points that lead to an accurate estimation of PGR. For selecting grasp configurations, the PGR computation approach is not robust because assumes that measured forces at the contact points do not have uncertainty. In addition to the heuristics, we propose a new uncertainty based metric, the coefficient of variation of PGR. The grasp selection experiments show that the coefficient of variation provides similar results to the pose variation metric. The grasp selection that uses the uncertainty based computation of PGR find more stable contact points than the maximization of the conventional PGR. Article highlights Development of new heuristics for computation ofgrasp metrics of underactuated hands. Definition of uncertainty-based metrics for grasp se-lection Reduction of reality gap for physics-based graspingmetrics of underactuated hands

Uncertainty and heuristics for underactuated hands: grasp pose selection based on the ppotential grasp robustness metric