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%.

翻译：我们考虑一个家庭环境中的服务机器人，需要依次处理一系列高层任务。现有的大多数任务规划器由于缺乏对后续任务的认知，会孤立地解决每个任务，从而可能无意中引入副作用，使后续任务的成本更高。为了降低完成所有任务的总体成本，我们提出机器人必须预判其行为对未来任务可能产生的影响。因此，我们引入了预期性规划：一种利用图神经网络对预期未来成本进行估计，从而增强基于模型的任务规划的方法。我们的方法引导机器人采取鼓励准备和整理的行为，降低长期规划场景中的总体成本。我们在积木世界环境中评估了该方法，结果表明，与未采用预期性规划的规划相比，我们的方法将总体规划成本降低了5%。此外，如果赋予机器人预先准备环境的机会（预期性规划的一种特殊情况），我们的规划器可将总体成本降低11%。