Motions obtaining of multi-degree-freedom underwater robot by using reinforcement learning algorithms

Youkun Han; Hajime Kimura

doi:10.1109/TENCON.2010.5686136

Motions obtaining of multi-degree-freedom underwater robot by using reinforcement learning algorithms

Youkun Han, Hajime Kimura

Ship and Marine Dynamics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

This paper deals with motions obtaining of an underwater robot arm which have multi-degree of freedom by using reinforcement learning algorithms. A natural gradient Actor-Critic algorithm which uses Eligibility Traces is applied to the robot arm. In this algorithm, motion planning problems are modeled as finite state Markov decision processes. The robot arm is developed to have 4 joints, each joint consists 1 servo motor. The experiment results show the robot arm successfully learning to swim by feasible learning steps.

Original language	English
Title of host publication	TENCON 2010 - 2010 IEEE Region 10 Conference
Pages	1498-1502
Number of pages	5
DOIs	https://doi.org/10.1109/TENCON.2010.5686136
Publication status	Published - Dec 1 2010
Event	2010 IEEE Region 10 Conference, TENCON 2010 - Fukuoka, Japan Duration: Nov 21 2010 → Nov 24 2010

Other

Other	2010 IEEE Region 10 Conference, TENCON 2010
Country/Territory	Japan
City	Fukuoka
Period	11/21/10 → 11/24/10

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TENCON.2010.5686136

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title = "Motions obtaining of multi-degree-freedom underwater robot by using reinforcement learning algorithms",

abstract = "This paper deals with motions obtaining of an underwater robot arm which have multi-degree of freedom by using reinforcement learning algorithms. A natural gradient Actor-Critic algorithm which uses Eligibility Traces is applied to the robot arm. In this algorithm, motion planning problems are modeled as finite state Markov decision processes. The robot arm is developed to have 4 joints, each joint consists 1 servo motor. The experiment results show the robot arm successfully learning to swim by feasible learning steps.",

author = "Youkun Han and Hajime Kimura",

year = "2010",

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doi = "10.1109/TENCON.2010.5686136",

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AU - Kimura, Hajime

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N2 - This paper deals with motions obtaining of an underwater robot arm which have multi-degree of freedom by using reinforcement learning algorithms. A natural gradient Actor-Critic algorithm which uses Eligibility Traces is applied to the robot arm. In this algorithm, motion planning problems are modeled as finite state Markov decision processes. The robot arm is developed to have 4 joints, each joint consists 1 servo motor. The experiment results show the robot arm successfully learning to swim by feasible learning steps.

AB - This paper deals with motions obtaining of an underwater robot arm which have multi-degree of freedom by using reinforcement learning algorithms. A natural gradient Actor-Critic algorithm which uses Eligibility Traces is applied to the robot arm. In this algorithm, motion planning problems are modeled as finite state Markov decision processes. The robot arm is developed to have 4 joints, each joint consists 1 servo motor. The experiment results show the robot arm successfully learning to swim by feasible learning steps.

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M3 - Conference contribution

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BT - TENCON 2010 - 2010 IEEE Region 10 Conference

T2 - 2010 IEEE Region 10 Conference, TENCON 2010

Y2 - 21 November 2010 through 24 November 2010

ER -

Motions obtaining of multi-degree-freedom underwater robot by using reinforcement learning algorithms

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