  Wheelchair-mounted robotic arms (and other assistive robots) should help
their users perform everyday tasks. One way robots can provide this assistance
is shared autonomy. Within shared autonomy, both the human and robot maintain
control over the robot's motion: as the robot becomes confident it understands
what the human wants, it increasingly intervenes to automate the task. But how
does the robot know what tasks the human may want to perform in the first
place? Today's shared autonomy approaches often rely on prior knowledge: for
example, the robot must know the set of possible human goals a priori. In the
long-term, however, this prior knowledge will inevitably break down -- sooner
or later the human will reach for a goal that the robot did not expect. In this
paper we propose a learning approach to shared autonomy that takes advantage of
repeated interactions. Learning to assist humans would be impossible if they
performed completely different tasks at every interaction: but our insight is
that users living with physical disabilities repeat important tasks on a daily
basis (e.g., opening the fridge, making coffee, and having dinner). We
introduce an algorithm that exploits these repeated interactions to recognize
the human's task, replicate similar demonstrations, and return control when
unsure. As the human repeatedly works with this robot, our approach continually
learns to assist tasks that were never specified beforehand: these tasks
include both discrete goals (e.g., reaching a cup) and continuous skills (e.g.,
opening a drawer). Across simulations and an in-person user study, we
demonstrate that robots leveraging our approach match existing shared autonomy
methods for known goals, and outperform imitation learning baselines on new
tasks. See videos here: https://youtu.be/Plh4t3wQeIA
