Connectionist and neural net implementations of a robotic grasp generator

Sharon A. Stansfield

doi:10.1117/12.58587

1 March 1992 Connectionist and neural net implementations of a robotic grasp generator

Sharon A. Stansfield

Proceedings Volume 1708, Applications of Artificial Intelligence X: Machine Vision and Robotics; (1992) https://doi.org/10.1117/12.58587
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

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.

Citation Download Citation

Sharon A. Stansfield "Connectionist and neural net implementations of a robotic grasp generator", Proc. SPIE 1708, Applications of Artificial Intelligence X: Machine Vision and Robotics, (1 March 1992); https://doi.org/10.1117/12.58587

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