Robot-assisted navigation is a perfect example of a class of applications requiring flexible control approaches. When the human is reliable, the robot should concede space to their initiative. When the human makes inappropriate choices the robot controller should kick-in guiding them towards safer paths. Shared authority control is a way to achieve this behaviour by deciding online how much of the authority should be given to the human and how much should be retained by the robot. An open problem is how to evaluate the appropriateness of the human's choices. One possible way is to consider the deviation from an ideal path computed by the robot. This choice is certainly safe and efficient, but it emphasises the importance of the robot's decision and relegates the human to a secondary role. In this paper, we propose a different paradigm: a human's behaviour is correct if, at every time, it bears a close resemblance to what other humans do in similar situations. This idea is implemented through the combination of machine learning and adaptive control. The map of the environment is decomposed into a grid. In each cell, we classify the possible motions that the human executes. We use a neural network classifier to classify the current motion, and the probability score is used as a hyperparameter in the control to vary the amount of intervention. The experiments collected for the paper show the feasibility of the idea. A qualitative evaluation, done by surveying the users after they have tested the robot, shows that the participants preferred our control method over a state-of-the-art visco-elastic control.

翻译：机器人辅助导航是需要灵活控制方法的一类应用的典型范例。当人类操作可靠时，机器人应让位于其主动性；当人类做出不当选择时，机器人控制器应介入并引导其走向更安全的路径。共享权限控制通过在线决策如何分配人类与机器人的控制权，实现了这一行为。一个悬而未决的问题是如何评估人类选择的适当性。一种可能的方法是考虑与机器人计算出的理想路径之间的偏差。这种选择固然安全高效，但强化了机器决策的主导地位，将人类降级为辅助角色。本文提出了一种不同的范式：若人类的行为在任意时刻都与他人处于类似情境时的行为高度相似，则该行为被认为是正确的。这一思想通过机器学习与自适应控制的结合得以实现。我们将环境地图分解为网格，在每个网格单元中对人类可能执行的运动进行分类。采用神经网络分类器对当前运动进行分类，并将概率得分作为控制中的超参数来调节干预程度。论文收集的实验结果表明了该思想的可行性。通过用户测试机器人后的问卷调查进行的定性评估显示，参与者更倾向于我们的控制方法，而非当前最先进的粘弹性控制方法。