Autonomous robots are increasingly utilized in realistic scenarios with multiple complex tasks. In these scenarios, there may be a preferred way of completing all of the given tasks, but it is often in conflict with optimal execution. Recent work studies preference-based planning, however, they have yet to extend the notion of preference to the behavior of the robot with respect to each task. In this work, we introduce a novel notion of preference that provides a generalized framework to express preferences over individual tasks as well as their relations. Then, we perform an optimal trade-off (Pareto) analysis between behaviors that adhere to the user's preference and the ones that are resource optimal. We introduce an efficient planning framework that generates Pareto-optimal plans given user's preference by extending A* search. Further, we show a method of computing the entire Pareto front (the set of all optimal trade-offs) via an adaptation of a multi-objective A* algorithm. We also present a problem-agnostic search heuristic to enable scalability. We illustrate the power of the framework on both mobile robots and manipulators. Our benchmarks show the effectiveness of the heuristic with up to 2-orders of magnitude speedup.

翻译：自主机器人越来越多地应用于包含多个复杂任务的实际场景中。在这些场景中，可能存在一种完成所有给定任务的偏好方式，但这种方式往往与最优执行相冲突。近期研究探讨了基于偏好的规划，然而，尚未将偏好概念扩展到机器人针对每个任务的行为层面。在本工作中，我们引入了一种新颖的偏好概念，提供了一个通用框架来表达对个体任务及其关系的偏好。随后，我们对符合用户偏好的行为与资源最优行为进行了最优权衡（帕累托）分析。我们提出了一种高效的规划框架，通过扩展A*搜索生成符合用户偏好的帕累托最优规划。此外，我们展示了一种通过改进多目标A*算法来计算完整帕累托前沿（所有最优权衡的集合）的方法。我们还提出了一种与问题无关的搜索启发式方法以提升可扩展性。我们通过移动机器人和机械臂实例展示了该框架的能力。基准测试表明，该启发式方法可带来高达两个数量级的加速效果。