Learning mobile robot motion control from demonstrated primitives and human feedback

Brenna Argall*, Brett Browning, Manuela Veloso

*Corresponding author for this work

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

4 Scopus citations

Abstract

Task demonstration is one effective technique for developing robot motion 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 corrects 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 policy 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.

Original languageEnglish (US)
Title of host publicationRobotics Research - The 14th International Symposium ISRR
Pages417-432
Number of pages16
EditionSTAR
DOIs
StatePublished - Jun 9 2011
Event14th International Symposium of Robotic Research, ISRR 2009 - Lucerne, Switzerland
Duration: Aug 31 2009Sep 3 2009

Publication series

NameSpringer Tracts in Advanced Robotics
NumberSTAR
Volume70
ISSN (Print)1610-7438
ISSN (Electronic)1610-742X

Other

Other14th International Symposium of Robotic Research, ISRR 2009
CountrySwitzerland
CityLucerne
Period8/31/099/3/09

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Artificial Intelligence

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