Learning mobile robot motion control from demonstrated primitives and human feedback

Brenna Argall, Brett Browning, and Manuela Veloso
the 14th International Symposium on Robotics Research (ISRR09), 2009.


Download
  • Adobe portable document format (pdf) (326KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
Task demonstration is one effective technique for developing robot mo- tion control policies. As tasks become more complex, however, demonstration can become more difficult. In this work we introduce a technique that uses corrective human feedback to build a policy able to perform an undemonstrated task from simpler policies learned from demonstration. Our algorithm first evaluates and cor- rects the execution of motion primitive policies learned from demonstration. The algorithm next corrects and enables the execution of a larger task built from these primitives. Within a simulated robot motion control domain, we validate that a pol- icy for an undemonstrated task is successfully built from motion primitives learned from demonstration under our approach. We show feedback to both aid and enable policy development, improving policy performance in success, speed and efficiency.

Keywords
Learning from demonstration, mobile robot, motion control, teacher feedback

Notes
Sponsor: the Boeing Corporation, BBNT Solutions (via the US Air Force), Qatar Foundation for Education, Science and Community Development
Associated Lab(s) / Group(s): MultiRobot Lab
Associated Project(s): Treasure Hunt: Pickup Teams

Text Reference
Brenna Argall, Brett Browning, and Manuela Veloso, "Learning mobile robot motion control from demonstrated primitives and human feedback," the 14th International Symposium on Robotics Research (ISRR09), 2009.

BibTeX Reference
@inproceedings{Argall_2009_6992,
   author = "Brenna Argall and Brett Browning and Manuela Veloso",
   title = "Learning mobile robot motion control from demonstrated primitives and human feedback",
   booktitle = "the 14th International Symposium on Robotics Research (ISRR09)",
   year = "2009",
}