Shared autonomy enables robots to infer user intent and assist in
accomplishing it. But when the user wants to do a new task that the robot does
not know about, shared autonomy will hinder their performance by attempting to
assist them with something that is not their intent. Our key idea is that the
robot can detect when its repertoire of intents is insufficient to explain the
user's input, and give them back control. This then enables the robot to
observe unhindered task execution, learn the new intent behind it, and add it
to this repertoire. We demonstrate with both a case study and a user study that
our proposed method maintains good performance when the human's intent is in
the robot's repertoire, outperforms prior shared autonomy approaches when it
isn't, and successfully learns new skills, enabling efficient lifelong learning
for confidence-based shared autonomy.

共享自主能力使机器人能够推断用户的意图并协助完成任务。然而，当用户想要完成机器人不了解的新任务时，共享自主将阻碍其性能。本文提出机器人可以检测到其意图表述能力的不足并将控制权交还给用户来实现任务，从而使机器人能够进行无阻碍的任务执行并学习到新的意图来更新自己的意图库，并展示了该方法的良好表现和能够实现的全生命周期学习。