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

Abstract

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.