The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We present a novel application of a neural network to augment manual precision by cancelling involuntary motion. This method may be applied in microsurgery, using either a telerobotic approach or active compensation in a handheld instrument. A feedforward neural network is trained to input the measured trajectory of a handheld tool tip and output the intended trajectory. Use of the neural network decreases rms error in recordings from four subjects by an average of 43.9%.

Notes

Associated Center(s) / Consortia: Medical Robotics Technology Center

Associated Lab(s) / Group(s): Surgical Mechatronics Laboratory

Associated Project(s): Micron: Intelligent Microsurgical Instruments

Text Reference

Cameron Riviere and Pradeep Khosla, "Augmenting the human-machine interface: improving manual accuracy," IEEE International Conference on Robotics and Automation, April, 1997, pp. 3546 - 3550.

BibTeX Reference

@inproceedings{Riviere_1997_993,
   author = "Cameron Riviere and Pradeep Khosla",
   title = "Augmenting the human-machine interface: improving manual accuracy",
   booktitle = "IEEE International Conference on Robotics and Automation",
   pages = "3546 - 3550",
   month = "April",
   year = "1997",
   volume = "4",
}

The Robotics Institute

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