Teacher feedback to scaffold and refine demonstrated motion primitives on a mobile robot

Brenna D. Argall, Brett Browning, Manuela M. Veloso

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Task demonstration is an effective technique for developing robot motion control policies. As tasks become more complex, however, demonstration can become more difficult. In this work, we introduce an algorithm that uses corrective human feedback to build a policy able to perform a novel task, by combining simpler policies learned from demonstration. While some demonstration-based learning approaches do adapt policies with execution experience, few provide corrections within low-level motion control domains or to enable the linking of multiple of demonstrated policies. Here we introduce Feedback for Policy Scaffolding (FPS) as an algorithm that first evaluates and corrects the execution of motion primitive policies learned from demonstration. The algorithm next corrects and enables the execution of a more complex task constructed from these primitives. Key advantages of building a policy from demonstrated primitives is the potential for primitive policy reuse within multiple complex policies and the faster development of these policies, in addition to the development of complex policies for which full demonstration is difficult. Policy reuse under our algorithm is assisted by human teacher feedback, which also contributes to the improvement of policy performance. Within a simulated robot motion control domain we validate that, using FPS, a policy for a novel task is successfully built from motion primitives learned from demonstration. We show feedback to both aid and enable policy development, improving policy performance in success, speed and efficiency.

Original languageEnglish (US)
Pages (from-to)243-255
Number of pages13
JournalRobotics and Autonomous Systems
Volume59
Issue number3-4
DOIs
StatePublished - Mar 2011

Funding

The research is partly sponsored by the 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, the United States Department of the Interior under Grant No. NBCH-1040007 and the Qatar Foundation for Education, Science and Community Development . The views and conclusions contained in this document are solely those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the US government or any other entity.

Keywords

  • Demonstration learning
  • Policy reuse
  • Robot motion control
  • Teacher feedback

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • General Mathematics
  • Computer Science Applications

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