  Collaboration requires coordination, and we coordinate by anticipating our
teammates' future actions and adapting to their plan. In some cases, our
teammates' actions early on can give us a clear idea of what the remainder of
their plan is, i.e. what action sequence we should expect. In others, they
might leave us less confident, or even lead us to the wrong conclusion. Our
goal is for robot actions to fall in the first category: we want to enable
robots to select their actions in such a way that human collaborators can
easily use them to correctly anticipate what will follow. While previous work
has focused on finding initial plans that convey a set goal, here we focus on
finding two portions of a plan such that the initial portion conveys the final
one. We introduce $t$-\ACty{}: a measure that quantifies the accuracy and
confidence with which human observers can predict the remaining robot plan from
the overall task goal and the observed initial $t$ actions in the plan. We
contribute a method for generating $t$-predictable plans: we search for a full
plan that accomplishes the task, but in which the first $t$ actions make it as
easy as possible to infer the remaining ones. The result is often different
from the most efficient plan, in which the initial actions might leave a lot of
ambiguity as to how the task will be completed. Through an online experiment
and an in-person user study with physical robots, we find that our approach
outperforms a traditional efficiency-based planner in objective and subjective
collaboration metrics.
