Publications Details

Connectionist and neural net implementations of a robotic grasp generator

Stansfield, Sharon A.

This paper presents two parallel implementations of a knowledge-based robotic grasp generator. The grasp generator, originally developed as a rule-based system, embodies a knowledge of the associations between the features of an object and the set of valid hand shapes/arm configurations which may be used to grasp it. Objects are assumed to be unknown, with no a priori models available. The first part of this paper presents a `parallelization' of this rule base using the connectionist paradigm. Rules are mapped into a set of nodes and connections which represent knowledge about object features, grasps, and the required conditions for a given grasp to be valid for a given set of features. Having shown that the object and knowledge representations lend themselves to this parallel recasting, the second part of the paper presents a back propagation neural net implementation of the system that allows the robot to learn the associations between object features and appropriate grasps.