NDLI: Motor learning model using reinforcement learning with neural internal model

Content Provider	IEEE Xplore Digital Library
Author	Izawa, J. Kondo, T. Ito, K.
Copyright Year	2003
Description	Author affiliation: Dept. of Comput. Intelligence & Syst., Tokyo Inst. of Technol., Kanagawa, Japan (Izawa, J.; Kondo, T.; Ito, K.)
Abstract	The present paper proposes a learning control method for the musculoskeletal system of arm based on reinforcement learning. An optimization for the hand trajectory and muscle's force distribution is needed to acquire the reaching motion. The proposed architecture can acquire an optimized motion through learning the task. However, the biological control system composed of musculoskeletal system is not able to sense the state without time delay. The time delay causes instability of learning. The proposed scheme consists of the reinforcement learning part and neural internal model. Neural internal model is employed to compensate for the time delay by estimating the state of musculoskeletal system. Then, there must be a modeling error if some noise is included. Thus we introduce the minimum modeling error criterion for reinforcement learning, which gives not only the reduction of total muscle level but also the smoothness of the hand trajectory. The effectiveness and the biological plausibility of the present model is demonstrated by several simulations.
Sponsorship	IEEE Robotics & Autom. Soc. Nat. Sci. Council, Taiwan, Ministr. Educ., Taiwan
Starting Page	3146
Ending Page	3151
File Size	331069
Page Count	6
File Format	PDF
ISBN	0780377362
ISSN	10504729
DOI	10.1109/ROBOT.2003.1242074
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-14
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Muscles Biological system modeling Delay effects Musculoskeletal system Learning systems Biological control systems Elasticity Viscosity Jacobian matrices Indium tin oxide
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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