Policy Feedback for the Refinement of Learned Motion Control on a Mobile Robot

Brenna Dee Argall, Brett Browning, Manuela M. Veloso

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Motion control is fundamental to mobile robots, and the associated challenge in development can be assisted by the incorporation of execution experience to increase policy robustness. In this work, we present an approach that updates a policy learned from demonstration with human teacher feedback. We contribute advice-operators as a feedback form that provides corrections on state-action pairs produced during a learner execution, and Focused Feedback for Mobile Robot Policies (F3MRP) as a framework for providing feedback to rapidly-sampled policies. Both are appropriate for mobile robot motion control domains. We present a general feedback algorithm in which multiple types of feedback, including advice-operators, are provided through the F3MRP framework, and shown to improve policies initially derived from a set of behavior examples. A comparison to providing more behavior examples instead of more feedback finds data to be generated in different areas of the state and action spaces, and feedback to be more effective at improving policy performance while producing smaller datasets.

Original languageEnglish (US)
Pages (from-to)383-395
Number of pages13
JournalInternational Journal of Social Robotics
Issue number4
StatePublished - Nov 2012


  • Demonstration learning
  • Mobile robots
  • Motion control
  • Robot learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)
  • Social Psychology
  • Philosophy
  • Human-Computer Interaction
  • Electrical and Electronic Engineering


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