Connectionist Approach For Robot Grasp Planning

In this paper we show that learning schemes can be utilized to generate robot grasping points. These learning schemes can be based on the geometrical similarity of objects and the functional similarity of tasks. This approach will drastically increase the speed of the search process and enrich the system's knowledge base. A Neural Network model that acquires data from a sold modeling data base is suggested. This model combines the completeness of information provided by solid modeling with the uncertainty encountered in the grouping process to perform geometrical classification of objects.