IEICE TRANSACTIONS on Information
Learning and Control Model of the Arm for Loading
Summary :
We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.4 pp.705-716
- Publication Date
- 2009/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.705
- Type of Manuscript
- PAPER
- Category
- Biocybernetics, Neurocomputing
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Kyoungsik KIM, Hiroyuki KAMBARA, Duk SHIN, Yasuharu KOIKE, "Learning and Control Model of the Arm for Loading" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 4, pp. 705-716, April 2009, doi: 10.1587/transinf.E92.D.705.
Abstract: We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.705/_p
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@ARTICLE{e92-d_4_705,
author={Kyoungsik KIM, Hiroyuki KAMBARA, Duk SHIN, Yasuharu KOIKE, },
journal={IEICE TRANSACTIONS on Information},
title={Learning and Control Model of the Arm for Loading},
year={2009},
volume={E92-D},
number={4},
pages={705-716},
abstract={We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.},
keywords={},
doi={10.1587/transinf.E92.D.705},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Learning and Control Model of the Arm for Loading
T2 - IEICE TRANSACTIONS on Information
SP - 705
EP - 716
AU - Kyoungsik KIM
AU - Hiroyuki KAMBARA
AU - Duk SHIN
AU - Yasuharu KOIKE
PY - 2009
DO - 10.1587/transinf.E92.D.705
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2009
AB - We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.
ER -
English
