LSTM Learning of Inverse Dynamics with Contact in Various Environments

Daichi Furuta, Kyo Kutsuzawa, Sho Sakaino, Toshiaki Tsuji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A machine learning method has been introduced to solve the problem of inverse dynamics with contact in various environments. Conventional methods need multiple contact models to switch according to situations, while such methods have a difficulty in dealing with different environments. We propose a machine learning method that can handle various environments with a single learning model. We use long short-term memory as a learning model with high expression ability. From the verification, the proposed method showed higher performance than Gaussian processes. In addition, the performance of the model was improved by using training data collected under various environmental conditions.

Original languageEnglish
Title of host publicationProceedings - 2018 12th France-Japan and 10th Europe-Asia Congress on Mechatronics, Mecatronics 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages149-154
Number of pages6
ISBN (Electronic)9781538629826
DOIs
Publication statusPublished - 2018 Oct 17
Externally publishedYes
Event12th France-Japan and 10th Europe-Asia Congress on Mechatronics, Mecatronics 2018 - Tsu, Japan
Duration: 2018 Sep 102018 Sep 12

Publication series

NameProceedings - 2018 12th France-Japan and 10th Europe-Asia Congress on Mechatronics, Mecatronics 2018

Conference

Conference12th France-Japan and 10th Europe-Asia Congress on Mechatronics, Mecatronics 2018
CountryJapan
CityTsu
Period18/9/1018/9/12

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Mechanical Engineering
  • Artificial Intelligence
  • Modelling and Simulation
  • Orthopedics and Sports Medicine
  • Control and Optimization
  • Rehabilitation

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