  We consider a service robot in a household environment given a sequence of
high-level tasks one at a time. Most existing task planners, lacking knowledge
of what they may be asked to do next, solve each task in isolation and so may
unwittingly introduce side effects that make subsequent tasks more costly. In
order to reduce the overall cost of completing all tasks, we consider that the
robot must anticipate the impact its actions could have on future tasks. Thus,
we propose anticipatory planning: an approach in which estimates of the
expected future cost, from a graph neural network, augment model-based task
planning. Our approach guides the robot towards behaviors that encourage
preparation and organization, reducing overall costs in long-lived planning
scenarios. We evaluate our method on blockworld environments and show that our
approach reduces the overall planning costs by 5% as compared to planning
without anticipatory planning. Additionally, if given an opportunity to prepare
the environment in advance (a special case of anticipatory planning), our
planner improves overall cost by 11%.
