  In shared autonomy, a user and autonomous system work together to achieve
shared goals. To collaborate effectively, the autonomous system must know the
user's goal. As such, most prior works follow a predict-then-act model, first
predicting the user's goal with high confidence, then assisting given that
goal. Unfortunately, confidently predicting the user's goal may not be possible
until they have nearly achieved it, causing predict-then-act methods to provide
little assistance. However, the system can often provide useful assistance even
when confidence for any single goal is low (e.g. move towards multiple goals).
In this work, we formalize this insight by modelling shared autonomy as a
Partially Observable Markov Decision Process (POMDP), providing assistance that
minimizes the expected cost-to-go with an unknown goal. As solving this POMDP
optimally is intractable, we use hindsight optimization to approximate. We
apply our framework to both shared-control teleoperation and human-robot
teaming. Compared to predict-then-act methods, our method achieves goals
faster, requires less user input, decreases user idling time, and results in
fewer user-robot collisions.
