By Topic

Robotic imitation from human motion capture using Gaussian processes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Shon, A.P. ; Dept. of Comput. Sci. & Eng., Washington Univ., Seattle, WA ; Grochow, K. ; Rao, R.P.N.

Programming by demonstration, also called "imitation learning," offers the possibility of flexible, easily modifiable robotic systems. Full-fledged robotic imitation learning comprises many difficult subtasks. However, we argue that, at its core, imitation learning reduces to a regression problem. We propose a two-step framework in which an imitating agent first performs a regression from a high-dimensional observation space to a low-dimensional latent variable space. In the second step, the agent performs a regression from the latent variable space to a high-dimensional space representing degrees of freedom of its motor system. We demonstrate the validity of the approach by learning to map motion capture data from human actors to a humanoid robot. We also contrast use of several low-dimensional latent variable spaces, each covering a subset of agents' degrees of freedom, with use of a single, higher-dimensional latent variable space. Our findings suggest that compositing several regression models together yields qualitatively better imitation results than using a single, more complex regression model

Published in:

Humanoid Robots, 2005 5th IEEE-RAS International Conference on

Date of Conference:

5-5 Dec. 2005