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Modeling of endpoint feedback learning implemented through point-to-point learning control

Modeling of endpoint feedback learning implemented through point-to-point learning control
Modeling of endpoint feedback learning implemented through point-to-point learning control
In the last decade, several experiments were conducted to investigate human motor control behavior for the task of arm reaching, using only visual feedback of the final hand position at the end of each reaching motion. Current computational frameworks have yet to model that the humans learn to complete such a task by feedforward action based on the feedback of a displacement error at the end of past reaching motions. This paper demonstrates how such learning can be formulated as an optimization problem. By designing a cost function which weighs the tracking of the target and the smoothness of human motion, the constructed framework, implemented in the form of point-to-point learning control, inherently embeds the feedforward control and enables learning over repeated trials using only the available feedback from past observations, here the endpoint errors of a reaching motion trajectory. The proposed framework is able to reproduce the human learning behavior observed in experiments.
1063-6536
1576-1585
Zhou, S.-H.
f1482119-bc34-41bd-9b08-737ab2109d3e
Tan, Y.
5f06a398-5b4f-499d-a079-29b4bcf5f7fc
Oetomo, D.
a28f79d5-fc16-4aea-bd0e-08c73a94c8b9
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Burdet, E
5334361c-1c42-4368-8c28-4f6a95853275
Mareels, I
26d06f2d-c755-41ff-8d01-36603d2726cd
Zhou, S.-H.
f1482119-bc34-41bd-9b08-737ab2109d3e
Tan, Y.
5f06a398-5b4f-499d-a079-29b4bcf5f7fc
Oetomo, D.
a28f79d5-fc16-4aea-bd0e-08c73a94c8b9
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Burdet, E
5334361c-1c42-4368-8c28-4f6a95853275
Mareels, I
26d06f2d-c755-41ff-8d01-36603d2726cd

Zhou, S.-H., Tan, Y., Oetomo, D., Freeman, C.T., Burdet, E and Mareels, I (2016) Modeling of endpoint feedback learning implemented through point-to-point learning control. IEEE Transactions on Control Systems Technology, 25 (5), 1576-1585. (doi:10.1109/TCST.2016.2615083).

Record type: Article

Abstract

In the last decade, several experiments were conducted to investigate human motor control behavior for the task of arm reaching, using only visual feedback of the final hand position at the end of each reaching motion. Current computational frameworks have yet to model that the humans learn to complete such a task by feedforward action based on the feedback of a displacement error at the end of past reaching motions. This paper demonstrates how such learning can be formulated as an optimization problem. By designing a cost function which weighs the tracking of the target and the smoothness of human motion, the constructed framework, implemented in the form of point-to-point learning control, inherently embeds the feedforward control and enables learning over repeated trials using only the available feedback from past observations, here the endpoint errors of a reaching motion trajectory. The proposed framework is able to reproduce the human learning behavior observed in experiments.

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Accepted/In Press date: 18 September 2016
e-pub ahead of print date: 26 October 2016
Published date: 26 October 2016
Organisations: EEE

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Local EPrints ID: 361357
URI: http://eprints.soton.ac.uk/id/eprint/361357
ISSN: 1063-6536
PURE UUID: c57f3ac6-ca13-4d9f-9f3d-c2ab17b65a9c

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Date deposited: 18 Jan 2014 14:43
Last modified: 07 Oct 2020 06:19

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Contributors

Author: S.-H. Zhou
Author: Y. Tan
Author: D. Oetomo
Author: C.T. Freeman
Author: E Burdet
Author: I Mareels

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