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We consider a service robot in a household environment given a sequence ofhigh-level tasks one at a time. Most existing task planners, lacking knowledgeof what they may be asked to do next, solve each task in isolation and so mayunwittingly introduce side effects that make subsequent tasks more costly. Inorder to reduce the overall cost of completing all tasks, we consider that therobot must anticipate the impact its actions could have on future tasks. Thus,we propose anticipatory planning: an approach in which estimates of theexpected future cost, from a graph neural network, augment model-based taskplanning. Our approach guides the robot towards behaviors that encouragepreparation and organization, reducing overall costs in long-lived planningscenarios. We evaluate our method on blockworld environments and show that ourapproach reduces the overall planning costs by 5% as compared to planningwithout anticipatory planning. Additionally, if given an opportunity to preparethe environment in advance (a special case of anticipatory planning), ourplanner improves overall cost by 11%.

Anticipatory Planning: Improving Long-Lived Planning by Estimating Expected Cost of Future Tasks