多机器人在未知环境中协同完成任务，通过学习与规划结合提升效率。
Multi-Robot Learning-Informed Task Planning Under Uncertainty
- 用学习预测环境不确定性，结合模型规划长程协作
- 在大场景中实现1~3机器人高效任务调度，优于基线方法
- 真实家居环境下两台LoCoBot验证了方案可行性
我们希望多机器人团队在任务相关物体位置未知的情况下，以最短时间完成复杂任务。有效执行需要对任务物体可能位置进行长期推理，理解单个动作如何推动整体进展，并协调团队行动。该设定下的规划极为困难：即使部分信息已知，决定哪个机器人何时执行何种动作也充满挑战；不确定性导致每个动作可能产生多种结果，进一步加剧长时决策与协作的复杂性。为此，我们提出一种多机器人规划抽象框架，将学习用于估计环境中的不确定因素，同时采用基于模型的规划实现长周期协调。我们在大型ProcTHOR家庭环境中，对1、2和3个机器人的团队展示了该方法在多阶段任务规划上的高效性，性能超越竞争基线。此外，我们还在真实家居环境中使用两个LoCoBot移动机器人验证了该方法的有效性。
原文摘要 · Abstract (English)
We want a multi-robot team to complete complex tasks in minimum time where the locations of task-relevant objects are not known. Effective task completion requires reasoning over long horizons about the likely locations of task-relevant objects, how individual actions contribute to overall progress, and how to coordinate team efforts. Planning in this setting is extremely challenging: even when task-relevant information is partially known, coordinating which robot performs which action and when is difficult, and uncertainty introduces a multiplicity of possible outcomes for each action, which further complicates long-horizon decision-making and coordination. To address this, we propose a multi-robot planning abstraction that integrates learning to estimate uncertain aspects of the environment with model-based planning for long-horizon coordination. We demonstrate the efficient multi-stage task planning of our approach for 1, 2, and 3 robot teams over competitive baselines in large ProcTHOR household environments. Additionally, we demonstrate the effectiveness of our approach with a team of two LoCoBot mobile robots in real household settings.
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