Connectionist and neural net implementations of a robotic grasp generator
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 association 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 association between object features and appropriate grasps. 12 refs.
- Research Organization:
- Sandia National Labs., Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE; USDOE, Washington, DC (United States)
- DOE Contract Number:
- AC04-76DP00789
- OSTI ID:
- 5708308
- Report Number(s):
- SAND-91-1746C; CONF-920471-1; ON: DE92006832
- Resource Relation:
- Conference: International Society for Optical Engineering (SPIE) conference, Orlando, FL (United States), 20 Apr 1992
- Country of Publication:
- United States
- Language:
- English
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Robotic grasping of unknown objects: A knowledge-based approach
Robotic grasping of unknown objects: A knowledge-based approach
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