  We present stress-minimizing (SM) metric, a new metric of grasp qualities.
Unlike previous metrics that ignore the material of target objects, we assume
that target objects are made of homogeneous isotopic materials. SM metric
measures the maximal resistible external wrenches without causing fracture in
the target objects. Therefore, SM metric is useful for robot grasping valuable
and fragile objects. In this paper, we analyze the properties of this new
metric, propose grasp planning algorithms to generate globally optimal grasps
maximizing the SM metric, and compare the performance of the SM metric and a
conventional metric. Our experiments show that SM metric is aware of the
geometries of target objects while the conventional metric are not. We also
show that the computational cost of the SM metric is on par with that of the
conventional metric.
