Towards robot arm training in virtual reality using partial least squares regression

Benjamin Volmer, Adrien Verhulst, Masahiko Inami, Adam Drogemuller, Maki Sugimoto, Bruce H. Thomas

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Robot assistance can reduce the user's workload of a task. However, the robot needs to be programmed or trained on how to assist the user. Virtual Reality (VR) can be used to train and validate the actions of the robot in a safer and cheaper environment. In this paper, we examine how a robotic arm can be trained using Coloured Petri Nets (CPN) and Partial Least Squares Regression (PLSR). Based upon these algorithms, we discuss the concept of using the user's acceleration and rotation as a sufficient means to train a robotic arm for a procedural task in VR. We present a work-in-progress system for training robotic limbs using VR as a cost effective and safe medium for experimentation. Additionally, we propose PLSR data that could be considered for training data analysis.

本文言語English
ホスト出版物のタイトル26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1209-1210
ページ数2
ISBN(電子版)9781728113777
DOI
出版ステータスPublished - 2019 3
イベント26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019 - Osaka, Japan
継続期間: 2019 3 232019 3 27

出版物シリーズ

名前26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019 - Proceedings

Conference

Conference26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019
国/地域Japan
CityOsaka
Period19/3/2319/3/27

ASJC Scopus subject areas

  • 人間とコンピュータの相互作用
  • メディア記述

フィンガープリント

「Towards robot arm training in virtual reality using partial least squares regression」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル