Re-grasping behavior acquisition by evolutionary programming

The control of multi-fingered robot hands has been the subject of recent interest. To manipulate some object with it, it is necessary to determine the contact points and grasping forces at the fingertips, but it is difficult to optimize such manipulation parameters for performing effective manipulation. In this paper, we propose an acquisition method for re-grasping motion for a four-fingered robot hand using evolutionary programming (EP). In this case, EP determines not only when and where the fingers are moved, but also which finger is operated at the next contact point according to the attitude changes of object grasping with the grasping stability. Evolutionary programming is generally able to find an optimal solution without a supervisor after much iteration, which is almost impractical using a real robot. Therefore we control the real robot hand with the optimal controller that has been obtained in computer simulations. We show the effectiveness of the proposed acquisition method for the re-grasping motion with experimental results