Learning hand movements from markerless demonstrations for humanoid tasks

Ren Mao, Yezhou Yang, Cornelia Fermüller, Yiannis Aloimonos, John S. Baras

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

6 Scopus citations

Abstract

We present a framework for generating trajectories of the hand movement during manipulation actions from demonstrations so the robot can perform similar actions in new situations. Our contribution is threefold: 1) we extract and transform hand movement trajectories using a state-of-the-art markerless full hand model tracker from Kinect sensor data; 2) we develop a new bio-inspired trajectory segmentation method that automatically segments complex movements into action units, and 3) we develop a generative method to learn task specific control using Dynamic Movement Primitives (DMPs). Experiments conducted both on synthetic data and real data using the Baxter research robot platform validate our approach.

Original languageEnglish (US)
Title of host publication2014 IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014
PublisherIEEE Computer Society
Pages938-943
Number of pages6
Volume2015-February
ISBN (Electronic)9781479971749
DOIs
StatePublished - Feb 12 2015
Externally publishedYes
Event2014 14th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014 - Madrid, Spain
Duration: Nov 18 2014Nov 20 2014

Other

Other2014 14th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014
CountrySpain
CityMadrid
Period11/18/1411/20/14

    Fingerprint

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

Cite this

Mao, R., Yang, Y., Fermüller, C., Aloimonos, Y., & Baras, J. S. (2015). Learning hand movements from markerless demonstrations for humanoid tasks. In 2014 IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014 (Vol. 2015-February, pp. 938-943). [7041476] IEEE Computer Society. https://doi.org/10.1109/HUMANOIDS.2014.7041476