Reinforcement learning approach to acquisition of stable gaits for locomotion robots

Mikhail Mikhailovich Svinin, K. Yamada, K. Ueda

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

Emergence of motion patterns in locomotion robots is studied in this paper. Acquisition of stable periodical gaits can be organized by learning how to reach a goal position. Classifier systems are used for the sensory motor control of individual legs. During the learning process, the classifiers are implicitly coordinated by sharing the total sensor space of the robot. The proposed approach is tested under simulation and experiment on a special four-legged robot. It is show that periodical gaits emerge as a result of interaction between the four classifier systems.

Original languageEnglish
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume6
Publication statusPublished - Dec 1 1999
Event1999 IEEE International Conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics' - Tokyo, Jpn
Duration: Oct 12 1999Oct 15 1999

Fingerprint

Reinforcement learning
Classifiers
Robots
Sensors
Experiments

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Hardware and Architecture

Cite this

Reinforcement learning approach to acquisition of stable gaits for locomotion robots. / Svinin, Mikhail Mikhailovich; Yamada, K.; Ueda, K.

In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Vol. 6, 01.12.1999.

Research output: Contribution to journalConference article

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