Effective input order of dynamics learning tree

Chyon Hae Kim*, Shohei Hama, Ryo Hirai, Kuniyuki Takahashi, Hiroki Yamada, Tetsuya Ogata, Shigeki Sugano

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In this paper, we discuss about the learning performance of dynamics learning tree (DLT) while mainly focusing on the implementation on robot arms. We propose an input-order-designing method for DLT. DLT has been applied to the modeling of boat, vehicle, and humanoid robot. However, the relationship between the input order and the performance of DLT has not been investigated. In the proposed method, a developer is able to design an effective input order intuitively. The proposed method was validated in the model learning tasks on a simulated robot manipulator, a real robot manipulator, and a simulated vehicle. The first/second manipulator was equipped with flexible arm/finger joints that made uncertainty around the trajectories of manipulated objects. In all of the cases, the proposed method improved the performance of DLT.

Original languageEnglish
Pages (from-to)122-136
Number of pages15
JournalAdvanced Robotics
Issue number3
Publication statusPublished - 2018 Feb 1


  • Learning
  • drawing
  • humanoid robot
  • manipulation
  • modeling

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications


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