这篇论文教机器人做当前任务时也替别人着想，在家庭和餐厅测试里省了10%到17%的成本，挺实用的规划新思路。
论文提出礼貌性前瞻规划方法，让机器人在持久共享环境中执行任务时不仅考虑当前任务成本，还通过独立学习的成本估计器预估未来所有机器人的任务成本。在双机器人家庭环境中，该方法总成本比短视规划降低10.43%，比自私前瞻规划降低4.03%；在三机器人餐厅环境中，分别降低17.41%和13.24%。方法支持模块化部署，新增机器人只需训练其估计器。
Courteous Anticipation: Improving Long-Lived Task Planning in Persistent Shared Environments
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.