Journal
ROBOTICS AND AUTONOMOUS SYSTEMS
Volume 57, Issue 5, Pages 469-483Publisher
ELSEVIER
DOI: 10.1016/j.robot.2008.10.024
Keywords
Learning from demonstration; Robotics; Machine learning; Autonomous systems
Funding
- Boeing Corporation [CMU-BA-GTA-1]
- BBNT Solutions [950008572]
- Air Force [SA-8650-06-C-7606]
- Qatar Foundation for Education, Science and Community Development
Ask authors/readers for more resources
We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, anti contribute the foundations for a structure in which to categorize LfD research. Specifically, we analyze and categorize the multiple ways in which examples are gathered, ranging from teleoperation to imitation, as well as the various techniques for policy derivation, including matching functions, dynamics models and plans. To conclude we discuss LfD limitations and related promising areas for future research. (c) 2008 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available