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.

翻译：可变自主性赋予系统（如机器人）混合主动性，使其能够根据任务复杂度和周围环境调整自主水平。可变自主性解决了机器人规划中的两个主要问题：其一是人类在缺乏适当人因指标时难以持续保持对机器人任务的监控与干预专注度；其二是面对静态奖励结构时如何在不可预见且不确定的环境中实现任务成功。可变自主性领域的一个开放性问题在于开发鲁棒方法以实时动态平衡自主性与人工干预，从而在不可预测且动态变化的环境中确保最优性能与安全性。我们认为，通过引入基于规则的符号逻辑来处理不可预测的动态环境，有望使自主性调整更具情境可靠性；而利用混合主动性控制产生的数据为强化学习提供反馈，能进一步提升自主行为的效能与安全性。