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    Using Backpropagation with Temporal Windows to Learn the Dynamics of the CMU Direct-Drive Arm II

    Goldberg, Kenneth Y. and Pearlmutter, Barak A. (1988) Using Backpropagation with Temporal Windows to Learn the Dynamics of the CMU Direct-Drive Arm II. In: Advances in Neural Information Processing Systems 1 (NIPS 1988). Morgan Kaufmann, p. 356. ISBN 1558600159

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    Computing the inverse dynamics of a robot arm is an active area of research in the control literature. We hope to learn the inverse dynamics by training a neural network on the measured response of a physical arm. The input to the network is a temporal window of measured positions; output is a vector of torques. We train the network on data measured from the first two joints of the CMU Direct-Drive Arm II as it moves through a randomly-generated sample of "pick-and-place" trajectories. We then test generalization with a new trajectory and compare its output with the torque measured at the physical arm. The network is shown to generalize with a root mean square error/standard deviation (RMSS) of 0.10. We interpreted the weights of the network in terms of the velocity and acceleration filters used in conventional control theory.

    Item Type: Book Section
    Keywords: Backpropagation; Temporal Windows; CMU Direct-Drive Arm II; robot arm;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 10248
    Depositing User: Barak Pearlmutter
    Date Deposited: 28 Nov 2018 14:13
    Publisher: Morgan Kaufmann
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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