A dynamic autonomy allocation framework automatically shifts how much control lies with the human versus the robotics autonomy, for example based on factors such as environmental safety or user preference. To investigate the question of which factors should drive dynamic autonomy allocation, we perform a human subject study to collect ground truth data that shifts between levels of autonomy during shared-control robot operation. Information streams from the human, the interaction between the human and the robot, and the environment are analyzed. Machine learning methods -- both classical and deep learning -- are trained on this data. An analysis of information streams from the human-robot team suggests features which capture the interaction between the human and the robotics autonomy are the most informative in predicting when to shift autonomy levels. Even the addition of data from the environment does little to improve upon this predictive power. The features learned by deep networks, in comparison to the hand-engineered features, prove variable in their ability to represent shift-relevant information. This work demonstrates the classification power of human-only and human-robot interaction information streams for use in the design of shared-control frameworks, and provides insights into the comparative utility of various data streams and methods to extract shift-relevant information from those data.

中文翻译：

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人机信息流分析以通知动态自治分配

动态自治分配框架会根据环境安全或用户偏好等因素自动改变人类与机器人自治的控制程度。为了研究哪些因素应该驱动动态自主分配的问题，我们进行了一项人类主题研究，以收集在共享控制机器人操作期间在自主级别之间转换的地面实况数据。分析来自人类、人类与机器人之间的交互以及环境的信息流。机器学习方法——经典学习和深度学习——都是在这些数据上进行训练的。对来自人机团队的信息流的分析表明，捕捉人与机器人自主之间交互的特征在预测何时改变自主级别方面最具信息量。即使添加来自环境的数据也无助于提高这种预测能力。与手工设计的特征相比，深度网络学习的特征在表示与移位相关的信息的能力方面证明是可变的。这项工作展示了用于共享控制框架设计的仅人类和人机交互信息流的分类能力，并提供了对各种数据流和方法的比较效用的见解，以从这些数据中提取与班次相关的信息. 即使添加来自环境的数据也无助于提高这种预测能力。与手工设计的特征相比，深度网络学习的特征证明了它们表示与移位相关信息的能力是可变的。这项工作展示了用于共享控制框架设计的仅人类和人机交互信息流的分类能力，并提供了对各种数据流和方法的比较效用的见解，以从这些数据中提取与班次相关的信息. 即使添加来自环境的数据也无助于提高这种预测能力。与手工设计的特征相比，深度网络学习的特征证明了它们表示与移位相关信息的能力是可变的。这项工作展示了用于共享控制框架设计的仅人类和人机交互信息流的分类能力，并提供了对各种数据流和方法的比较效用的见解，以从这些数据中提取与班次相关的信息.