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Genetic algorithms for dexterous manipulation Seminar in Machine Learning June 2005 Outline • • • • • Dexterous manipulation Robot hands Genetic algorithms and Genetic programming Biologically inspired robot grasping using GP A biologically inspired fitness function for robot grasping • Conclusions Dexterous manipulation The capability of changing the position and orientation of the manipulated object from a given reference configuration to a different one, arbitrarily chosen within the hand workspace • Dexterous manipulation can be used to increase flexibility and provide richer manipulation patterns Robot hands • Reliability and compactness and the developments of suitable sensors, in particular effective fingertip sensors • Grasp synthesis/Grasp planning • Grasp stability - how many fingers are necessary in order to stably grasp a given object and where should these fingers be placed under various conditions Robot hands • Form and Force Closure Form Closure: the ability of a grasp to prevent motions of the object relying on only unilateral, frictionless contact constraints Force Closure: the situation where motions of the object are constrained by suitably large contact forces of the grasp (usually considering friction) GA and GP • Created by John Henry Holland in 1960’s • AI method that mimics the natural biological processes • Transforms a set of individual mathematical objects by using the Darwinian principle of survival of the fittest Crossover, genetic recombination Mutation Reproduction GA/GP for dexterous manipulation • Initial population of candidate grasps • Each member of the population (grasp) is evaluated for its fitness • The GA evolves the population through a number of generations of candidate grasps • The evolution is guided by a fitness function provided by the user • In the end a final superior grasp population is generated Biologically inspired robot grasping using GP Robot grasping • Use of a GA to solve the grasp synthesis problem for multifingered robot hands • Select a “best” grasp of an object given some information about the object geometry and some user-defined fitness functions • Fitness function is used by the GA to evolve populations of candidate grasps • Provide the operator with a general grasp selection planner which can select and preview candidate grasps across a wide range of objects and tasks Robot grasping • Example with a softball to be grasped by the threefingered Barrett Hand • The program begins with a description of the object to be grasped and an open hand • The program placed imaginary spheres onto the fingertips of the hand • Intersection between the spheres and the object are later used to evaluate when the fingers are in contact and also to adjust the tightness of a grasp • The program positions the object roughly central to the workspace of the hand Robot grasping • Barrett hand with object Robot grasping • Grasp population member and evolution to one-finger contact Robot grasping • Evolution to two-finger and three-finger contact Robot grasping • Final grasp Robot grasping • Grasping procedure A biologically Inspired Fitness Function for Robot Grasping Fitness Function for Grasping • Solutions represented in a tree format • The root node represents the entire hand • Each of the node that branches out represents a different finger • The finger nodes have three additional nodes that represent the position of each joint Fitness Function for Grasping • Representation trees Fitness Function for Grasping • Mutation Fitness Function for Grasping • Crossover Fitness Function for Grasping • Virtual spheres Fitness Function for Grasping • Triangle areas and triangle angles Fitness Function for Grasping • Multiple object planes Fitness Function for Grasping • Raccoon grasping Fitness Function for Grasping • Finger intersections Fitness Function for Grasping • Pseudo equation for the fitness function Fitness function = Area of intersection of virtual spheres (sum in square inches) + Triangle area (in square inches) + Triangle angles (sum in radians) + Number of contact planes (Maximum=9) + Angle between fingers and object (sum in radians) A biologically Inspired Fitness Function for Robot Grasping • Grasping a long object A biologically Inspired Fitness Function for Robot Grasping • Grasping a long object with different orientation... A biologically Inspired Fitness Function for Robot Grasping • Adjusting the fitness function itself to different kinds of objects, raccoon style grasp Conclusions • An approach for guiding grasp selection choices for multifingered robot hands based on GA and GP • Ability of the GA and GP to arrive at sensible grasps from an initial semi-random set of candidate grasps • The approach can result in grasps which would not have been initially selected by the user