A proposal of extracting of motion primitives by analyzing tracked data of hand motion from human demonstration

Ngoc Hung Pham, Takashi Yoshimi

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

2 Citations (Scopus)

Abstract

This paper presents a proposal to achieve motion primitives in the execution of manipulation actions from human demonstration. Human hand motion contains the most important information in the execution of manipulation actions. We design a method by using Kinect sensor to capture hand motion of each demonstrated action with a three-color-marker glove. The hand motion tracking data is calculated with three types: hand 3D position, hand orientation and hand states. Then, these tracked data is segmented to extract the motion primitives which then are used for building robot program that executes the action. We categorize three types of motion primitives including translation, rotation and state changing. In this study, we combine segmentation techniques based on mean square velocity and the change of hand state to extract the primitives of translation and state changing in the execution of action 'pick a cup'. In the experiment, we implement our design for tracking hand motion and analyze the tracked data to confirm our proposed segmentation techniques.

Original languageEnglish
Title of host publication47th International Symposium on Robotics, ISR 2016
PublisherVDE Verlag GmbH
Pages467-472
Number of pages6
ISBN (Electronic)9783800742318
Publication statusPublished - 2016
Event47th International Symposium on Robotics, ISR 2016 - Munich, Germany
Duration: 2016 Jun 212016 Jun 22

Other

Other47th International Symposium on Robotics, ISR 2016
CountryGermany
CityMunich
Period16/6/2116/6/22

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Building and Construction

Fingerprint Dive into the research topics of 'A proposal of extracting of motion primitives by analyzing tracked data of hand motion from human demonstration'. Together they form a unique fingerprint.

Cite this