机器人提前规划可显著降低长期任务成本，尤其在复杂家居环境。
Anticipatory Planning for Performant Long-Lived Robot in Large-Scale Home-Like Environments
- 用图神经网络建模环境，预估未来任务的开销。
- 在家庭和餐厅场景中任务成本分别降低31.5%和42.5%。
- 适合需要长期运行的智能机器人系统开发。
我们研究机器人在持续的大规模居家类环境中逐个接收任务时的长期任务规划问题。现有规划器常采取短视策略，仅关注当前目标，忽视动作对后续任务的影响。抗预见性规划通过最小化当前任务成本与未来任务预期成本之和，提升长期表现。然而，大规模环境中资产数量庞大，使学习与规划难以扩展。本文提出一种基于模型的抗预见性任务规划框架，利用受3D场景图启发的图神经网络，学习环境关键属性以估计状态预期成本，并采用采样方法实现可扩展的规划。实验表明，该规划器在家庭与餐厅场景中分别降低任务序列成本5.38%和31.5%；若提前使用模型准备，成本降幅可达40.6%和42.5%。
原文摘要 · Abstract (English)
We consider the setting where a robot must complete a sequence of tasks in a persistent large-scale environment, given one at a time. Existing task planners often operate myopically, focusing solely on immediate goals without considering the impact of current actions on future tasks. Anticipatory planning, which reduces the joint objective of the immediate planning cost of the current task and the expected cost associated with future subsequent tasks, offers an approach for improving long-lived task planning. However, applying anticipatory planning in large-scale environments presents significant challenges due to the sheer number of assets involved, which strains the scalability of learning and planning. In this research, we introduce a model-based anticipatory task planning framework designed to scale to large-scale realistic environments. Our framework uses a GNN in particular via a representation inspired by a 3D Scene Graph to learn the essential properties of the environment crucial to estimating the state's expected cost and a sampling-based procedure for practical large-scale anticipatory planning. Our experimental results show that our planner reduces the cost of task sequence by 5.38% in home and 31.5% in restaurant settings. If given time to prepare in advance using our model reduces task sequence costs by 40.6% and 42.5%, respectively.
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