An experimental approach to robotic grasping using a connectionist architecture and generic grasping functions

Abstract

An experimental approach to robotic grasping is presented. This approach is based on developing a generic representation of grasping rules, which allows learning them from experiments between the object and the robot. A modular connectionist design arranged in subsumption layers is used to provide a mapping between sensory inputs and robot actions. Reinforcement feedback is used to select between different grasping rules and to reduce the number of failed experiments. This is particularly critical for applications in the personal service robot environment. Simulated experiments on a 15-object database show that the system is capable of learning grasping rules for each object in a finite number of experiments as well as generalizing from experiments on one object to grasping from another.