Generating Optimal Grasps Under A Stress-Minimizing Metric
This addresses the challenge of safely grasping valuable and fragile objects in robotics, representing an incremental improvement with a new metric.
The authors tackled the problem of robot grasping fragile objects by introducing a stress-minimizing metric that accounts for object material and geometry to prevent fracture, showing it performs comparably in computational cost to conventional metrics.
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.