Trajectory planning by variable length chunk of sequence-to-sequence using hierarchical decoder

Tetsugaku Okamoto, Kyo Kutsuzawa, Sho Sakaino, Toshiaki Tsuji

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

1 Citation (Scopus)

Abstract

Object manipulation is one of the most important issues for robots supporting humans. However, trajectory planning for dynamic manipulation is a difficult issue due to dynamic constraints. This paper deals with trajectory planning for dynamic manipulation using sequence-to-sequence (seq2seq) models. In a conventional method, multiple samples are chunked in order to reduce the length of the time series. However, as the chunk size is fixed, accuracy may be reduced and trajectories are truncated. Therefore, we verified the effects of these. We solved these problems by a seq2seq model using a hierarchical decoder. As a result, proposed model was able to generate a trajectory more accurate than the conventional method.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages209-214
Number of pages6
ISBN (Electronic)9781538619469
DOIs
Publication statusPublished - 2018 Jun 1
Externally publishedYes
Event15th IEEE International Workshop on Advanced Motion Control, AMC 2018 - Tokyo, Japan
Duration: 2018 Mar 92018 Mar 11

Publication series

NameProceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018

Conference

Conference15th IEEE International Workshop on Advanced Motion Control, AMC 2018
Country/TerritoryJapan
CityTokyo
Period18/3/918/3/11

Keywords

  • Deep neural network
  • Robot
  • Trajectory planning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Mechanical Engineering
  • Control and Optimization

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