Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems
Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems
复制标题
机器人中的任务规划：基于 PDDL 和 ASP 的系统的实证比较
DOI：
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发表时间：
2018
影响因子：
3
通讯作者：
P. Stone
中科院分区：
文献类型：
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作者：
Yuqian Jiang;Shiqi Zhang;Piyush Khandelwal;P. Stone
机器人需要任务规划算法来对动作进行排序，以实现通过单独动作不可能实现的目标。智能机器人从业者可以使用现成的任务规划器来解决各种规划问题。然而，存在许多不同的规划器，每个规划器都有不同的优点和缺点，并且对于哪个规划器最适合应用于给定问题没有通用规则。在本研究中，我们根据经验比较了使用规划领域描述语言 (PDDL) 或答案集编程 (ASP) 作为底层操作语言的最先进规划器的性能。 PDDL 是为任务规划而设计的，基于 PDDL 的规划器广泛用于各种规划问题。 ASP 专为知识密集型推理而设计，但也可用于解决任务规划问题。给定尽可能相似的域编码，我们发现基于 PDDL 的规划器在解决方案较长的问题上表现更好，而基于 ASP 的规划器在具有大量对象的任务或需要复杂推理来推理动作前提条件和效果的任务上表现更好。由此产生的分析可以为特定机器人任务规划领域的通用规划系统的选择提供信息。
Robots need task planning algorithms to sequence actions toward accomplishing goals that are impossible through individual actions. Off-the-shelf task planners can be used by intelligent robotics practitioners to solve a variety of planning problems. However, many different planners exist, each with different strengths and weaknesses, and there are no general rules for which planner would be best to apply to a given problem. In this study, we empirically compare the performance of state-of-the-art planners that use either the planning domain description language (PDDL) or answer set programming (ASP) as the underlying action language. PDDL is designed for task planning, and PDDL-based planners are widely used for a variety of planning problems. ASP is designed for knowledge-intensive reasoning, but can also be used to solve task planning problems. Given domain encodings that are as similar as possible, we find that PDDL-based planners perform better on problems with longer solutions, and ASP-based planners are better on tasks with a large number of objects or tasks in which complex reasoning is required to reason about action preconditions and effects. The resulting analysis can inform selection among general-purpose planning systems for particular robot task planning domains.