A Survey of Robot Learning from Demonstration

Brenna Argall, Sonia Chernova, Manuela Veloso, and Brett Browning
Robotics and Autonomous Systems, Vol. 67, 2009, pp. 469-483.


Abstract
Wepresent 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, and 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.

Keywords
Learning from demonstration, Robotics, Machine learning, Autonomous systems

Notes
Sponsor: Boeing Corporation, under Grant No. CMU-BA-GTA-1, BBNT Solutions under subcontract No. 950008572, via prime Air Force contract No. SA-8650-06-C- 7606, Qatar Foundation for Education, Science and Community Development.

Text Reference
Brenna Argall, Sonia Chernova, Manuela Veloso, and Brett Browning, "A Survey of Robot Learning from Demonstration ," Robotics and Autonomous Systems, Vol. 67, 2009, pp. 469-483.

BibTeX Reference
@article{Argall_2009_6951,
   author = "Brenna Argall and Sonia Chernova and Manuela Veloso and Brett Browning",
   title = "A Survey of Robot Learning from Demonstration ",
   journal = "Robotics and Autonomous Systems",
   pages = "469-483",
   year = "2009",
   volume = "67",
}