経験から自律度を学習するコンピテンス認識システム
Learning to Optimize Autonomy in Competence-Aware Systems
半自律システムが経験を通じて自身の能力と人間フィードバックを学習し、状況に応じて最適な自律レベルを調整する枠組みを提案した論文。
著者: Connor Basich, Justin Svegliato, Kyle Hollins Wray, Stefan Witwicki, Joydeep Biswas, Shlomo Zilberstein
分類: cs.AI, cs.RO
原文アブストラクト
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.
