制御の交渉：ニューロシンボリックな可変自律性
Negotiating Control: Neurosymbolic Variable Autonomy
ルールベースの記号論理と強化学習を組み合わせ、ロボットが状況に応じて自律性と人間の介入のバランスを動的に調整する手法を提案した論文。
著者: Georgios Bakirtzis, Manolis Chiou, Andreas Theodorou
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Variable autonomy equips a system, such as a robot, with mixed initiatives such that it can adjust its independence level based on the task's complexity and the surrounding environment. Variable autonomy solves two main problems in robotic planning: the first is the problem of humans being unable to keep focus in monitoring and intervening during robotic tasks without appropriate human factor indicators, and the second is achieving mission success in unforeseen and uncertain environments in the face of static reward structures. An open problem in variable autonomy is developing robust methods to dynamically balance autonomy and human intervention in real-time, ensuring optimal performance and safety in unpredictable and evolving environments. We posit that addressing unpredictable and evolving environments through an addition of rule-based symbolic logic has the potential to make autonomy adjustments more contextually reliable and adding feedback to reinforcement learning through data from mixed-initiative control further increases efficacy and safety of autonomous behaviour.
