Biologically inspired robot grasping using genetic programming

This paper describes the innovative use of a genetic algorithm to solve the grasp synthesis problem for multifingered robot hands. The goal of our algorithm is to select a `best´ grasp of an object, given some information 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 biologically inspired both in the use of the genetic algorithm to `evolve´ populations of candidate grasps, and in the choice of fitness functions, which adapt intuition from nature to guide the evolution process