Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146)
DOI: 10.1109/robot.1998.680745
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The "feature CMAC": a neural-network-based vision system for robotic control

Abstract: A strategy for locating and grasping a target object in an unknown position using a robotic manipulator equipped with a CCD camera is described. Low-level trajectory and joint control during the grasping operation is handled by the manipulator S conventional motion contrcrller using target-pose data provided by an artificial-neural-network-based vision system. The Feature CMAC is a self-organizing neural network that eficiently transforms images of a target into estimates of its location and orientation. The a… Show more

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Cited by 15 publications
(5 citation statements)
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References 11 publications
(7 reference statements)
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“…CMAC has been applied as lookup tables to reproduce input-output functions defined by the kinematics of a robotic manipulator [80]. CMAC has been applied to the grasping control of a robotic manipulator using CCD camera images transposed to object locations that were passed to a conventional robot controller [81]. The look-up table representation is not consistent with biology however -it appears that motor adaptation does not involve the composition of look-up tables rather than the forming a full and adaptable model which can extrapolate beyond the initial training data [82].…”
Section: Models-backwards and Forwardsmentioning
confidence: 99%
“…CMAC has been applied as lookup tables to reproduce input-output functions defined by the kinematics of a robotic manipulator [80]. CMAC has been applied to the grasping control of a robotic manipulator using CCD camera images transposed to object locations that were passed to a conventional robot controller [81]. The look-up table representation is not consistent with biology however -it appears that motor adaptation does not involve the composition of look-up tables rather than the forming a full and adaptable model which can extrapolate beyond the initial training data [82].…”
Section: Models-backwards and Forwardsmentioning
confidence: 99%
“…Particular attention has been paid to the problem of vi suo-motor coordination [249], [250], [ 196], [235] , [113], [324], [73], [45], [188], [27], [83], in particular for eye-head and arm-eye systems. Sensor-based control is a very efficient method of overcoming the problems with robot model and environment uncertainties, because sensor capabilities help in the adaptation process without explicit control intervention.…”
Section: Sensor-based Robot Learning By Neural Networkmentioning
confidence: 99%
“…Visuo-motor models are either estimated analytically during the execution of the task on-line or learned off-line prior to the execution of the task. Artificial neural techniques can be used to learn the non-linear relationships between features in the images and manipulator joint angles (1), (2) . In Ref.…”
Section: Introductionmentioning
confidence: 99%
“…In Ref. (2), an approach used to train an uncalibrated industrial robot is proposed. A neural network provides the estimate of the pose of the target in the manipulator coordinate frame.…”
Section: Introductionmentioning
confidence: 99%