发表机构
George Mason University（乔治梅森大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究共享持久环境中机器人任务规划问题，提出礼貌预期规划方法，基于模型规划器联合最小化即时成本与预期未来成本，经独立估计器估计，在家庭和餐厅环境实验中相比其他规划降低了任务序列总成本。
AI 中文摘要
我们考虑一种任务规划场景，即共享持久环境的机器人从一个保留序列中依次被分配任务。标准任务规划器缺乏对未来任务的预见且不考虑其他机器人的约束，孤立地解决每个任务，留下会增加所有机器人未来成本的终端状态以及在长任务序列中复合的副作用。为降低序列成本，机器人必须预期其当前行动如何影响共享环境中所有机器人未来任务的性能。因此，我们提出礼貌预期规划，其中基于模型的规划器提出候选计划并选择能使所有机器人的即时成本和聚合预期未来成本联合最小化的计划，通过独立的每个机器人的学习估计器进行估计。这种分解式公式避免了组合式联合展开并支持模块化部署。我们在两个持久的PDDL领域进行评估，一个是具有相似能力但不同职责的机器人的家庭环境，另一个是机器人的不同能力会产生其他机器人无法解决的状态的餐厅环境。在长任务序列中，在双机器人家庭环境中，我们的规划器与近视规划相比总成本降低了10.43%，与自私预期规划相比降低了4.03%；在三机器人餐厅中，分别降低了17.41%和13.24%。
英文摘要
We consider a task planning scenario in which robots sharing a persistent environment are assigned tasks one at a time from a held-out sequence. Standard task planners, lacking foresight of future tasks and inconsiderate of others' constraints, solve each task in isolation, leaving terminal states that increase future cost for all, side effects that compound over lengthy task sequences. To reduce cost over the sequence, a robot must anticipate how its actions now may impact performance on future tasks for all robots sharing the environment. Therefore, we present courteous anticipatory planning, wherein a model-based planner proposes candidate plans and selects the one that jointly minimizes immediate cost and aggregated expected future cost across all robots, estimated via independent per-robot learned estimators. This factored formulation avoids combinatorial joint rollouts and supports modular deployment: adding a robot requires only training its own estimator. We evaluate in two persistent PDDL domains, a home environment with robots that have similar capabilities but different responsibilities, and a restaurant environment where robots' distinct capabilities create states that other robots lack the capability to resolve. During lengthy task sequences, our planner reduces total cost by 10.43% versus myopic and 4.03% versus selfish anticipatory planning in a two-robot home environment and by 17.41% and 13.24%, respectively, in a three-robot restaurant.
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