Carnegie Mellon University
Browse
file.pdf (308.5 kB)

Learning Robot Motion Control with Demonstration and Advice-Operators

Download (308.5 kB)
journal contribution
posted on 2008-01-01, 00:00 authored by Brenna D. Argall, Brett Browning, Manuela M. Veloso
As robots become more commonplace within society, the need for tools to enable non-robotics-experts to develop control algorithms, or policies, will increase. Learning from Demonstration (LfD) offers one promising approach, where the robot learns a policy from teacher task executions. Our interests lie with robot motion control policies which map world observations to continuous low-level actions. In this work, we introduce Advice-Operator Policy Improvement (AOPI) as a novel approach for improving policies within LfD. Two distinguishing characteristics of the A-OPI algorithm are data source and continuous state-action space. Within LfD, more example data can improve a policy. In A-OPI, new data is synthesized from a student execution and teacher advice. By contrast, typical demonstration approaches provide the learner with exclusively teacher executions. A-OPI is effective within continuous state-action spaces because high level human advice is translated into continuous-valued corrections on the student execution. This work presents a first implementation of the AOPI algorithm, validated on a Segway RMP robot performing a spatial positioning task. A-OPI is found to improve task performance, both in success and accuracy. Furthermore, performance is shown to be similar or superior to the typical exclusively teacher demonstrations approach.

History

Publisher Statement

"©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "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. In most cases, these works may not be reposted without the explicit permission of the copyright holder."

Date

2008-01-01

Usage metrics

    Keywords

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC