Hybrid system for knowledge-based synthesis of robot grasps

This paper addresses the grasp synthesis problem arising in task planning for robot dextrous hands. To this purpose, a hybrid architecture is proposed, which relies on symbolic and subsymbolic computations to exploit heterogeneous sources of knowledge, such as practical experience learned through experiments with a real device, heuristic rules gained from human observation, geometric reasoning and, when applicable, analytical results. After a preliminary discussion of representation levels and techniques, the paper describes the design of a tool for selecting the feasible grasps of a robotic hand under various situations and for ranking them according to task-oriented criteria.