Learning to Optimize Autonomy in Competence-Aware Systems

Abstract

Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to deploy autonomous systems in domains that require occasional reliance on humans. This paradigm allows service robots or autonomous vehicles to operate at varying levels of autonomy and offer safety in situations that require human judgment. We propose an introspective model of autonomy that is learned and updated online through experience and dictates the extent to which the agent can act autonomously in any given situation. We define a competence-aware system (CAS) that explicitly models its own proficiency at different levels of autonomy and the available human feedback. A CAS learns to adjust its level of autonomy based on experience to maximize overall efficiency, factoring in the cost of human assistance. We analyze the convergence properties of CAS and provide experimental results for robot delivery and autonomous driving domains that demonstrate the benefits of the approach.

Agent-aware State Estimation in Autonomous Vehicles

“…Reliable estimation of the global state is critical to robust multi-agent systems. Centrally managed coordination signals, such as traffic lights for autonomous vehicles [1], [2], [3], radio beacons for autonomous aerial robots [4], [5], and distress signals for search and rescue operations [6], [7], are particularly important examples of shared global state. Improperly receiving or interpreting these signals can cause catastrophic failures.…”