Improving Competence via Iterative State Space Refinement

Despite considerable efforts by human designers, accounting for every unique situation that an autonomous robotic system deployed in the real world could face is often an infeasible task. As a result, many such deployed systems still rely on human assistance in various capacities to complete certain tasks while staying safe. Competence-aware systems (CAS) is a recently proposed model for reducing such reliance on human assistance while in turn optimizing the system's global autonomous operation by learning its own competence. However, such systems are limited by a fixed model of their environment and may perform poorly if their a priori planning model does not include certain features that emerge as important over the course of the system's deployment. In this paper, we propose a method for improving the competence of a CAS over time by identifying important state features missing from the system's model and incorporating them into its state representation, thereby refining its state space. Our approach exploits information that exists in the standard CAS model and adds no extra work to the human. The result is an agent that better predicts human involvement, improving its competence, reliability, and overall performance.