  Assistive robot arms try to 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 intervenes to
automate the task. But how does the robot know these tasks in the first place?
State-of-the-art approaches to shared autonomy often rely on prior knowledge.
For instance, the robot may need to know the human's potential goals
beforehand. During long-term interaction these methods will inevitably break
down -- sooner or later the human will attempt to perform a task that the robot
does not expect. Accordingly, in this paper we formulate an alternate approach
to shared autonomy that learns assistance from scratch. Our insight is that
operators repeat important tasks on a daily basis (e.g., opening the fridge,
making coffee). Instead of relying on prior knowledge, we therefore take
advantage of these repeated interactions to learn assistive policies. We
introduce SARI, an algorithm that recognizes the human's task, replicates
similar demonstrations, and returns control when unsure. We then combine
learning with control to demonstrate that the error of our approach is
uniformly ultimately bounded. We perform simulations to support this error
bound, compare our approach to imitation learning baselines, and explore its
capacity to assist for an increasing number of tasks. Finally, we conduct three
user studies with industry-standard methods and shared autonomy baselines,
including a pilot test with a disabled user. Our results indicate that learning
shared autonomy across repeated interactions matches existing approaches for
known tasks and outperforms baselines on new tasks. See videos of our user
studies here: https://youtu.be/3vE4omSvLvc
