Biologically Inspired Robot Grasping Using Genetic Programming

This paper describes the innovative use of a genetic al-gorithm to solve the grasp synthesis problem for mul-tifingered robot hands. The goal of our algorithm is to select a ‘best ’ grasp of an object, given some in-formation about the object geometry and some user-defined ‘fitness functions ’ which intuitively delineate ‘good ’ from ‘bad ’ grasp qualities. The fitness functions are used by the specially designed genetic algorithm, which iteratively selects the grasp. The approach is bi-ologically inspired, both in the use of the genetic algo-rithm to ‘evolve ’ populations of candidate grasps, and in the choice of fitness functions, which adapt intuition from nature to guide the evolution process.