Learning Mobile Robot Motion Control from Demonstration and Corrective Feedback

Abstract Robust motion control algorithms are fundamental to the successful, au-tonomous operation of mobile robots. Motion control is known to be a difficult problem, and is often dictated by a policy, or state-action mapping. In this chapter, we present an approach for the refinement of mobile robot motion control policies, that incorporates corrective feedback from a human teacher. The target applica-tion domain of this work is the low-level motion control of a mobile robot. Within such domains, the rapid sampling rate and continuous action space of policies are both key challenges to providing policy corrections. To address these challenges, we contribute advice-operators as a corrective feedback form suitable for providing continuous-valued corrections, and Focused Feedback For Mobile Robot Policies (F3MRP) as a framework suitable for providing feedback on policies sampled at a high frequency. Under our approach, policies refined through teacher feedback are initially derived using Learning from Demonstration (LfD) techniques, which gen-eralize a policy from example task executions by a teacher. We apply our techniques within the Advice-Operator Policy Improvement (A-OPI) algorithm, validated on