  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.
