研究传感器机器人任务的可描述性边界，揭示不同信息基础对任务定义的影响。
Limits of specifiability for sensor-based robotic planning tasks
- 提出统一符号体系，系统分析任务规范依赖的状态、动作、观测等信息基础
- 发现特定任务仅在特定信息组合下才可被准确描述
- 为复杂机器人任务设计提供理论依据，适合形式化方法研究者
目前已有大量基于形式化方法的技术用于描述和实现复杂的机器人任务，包括涉及多种丰富目标和时序扩展行为的任务。本文探讨了任务可描述性的边界，重点分析规范的精确语境——即规范是基于机器人的状态、动作与观测，还是其知识或其他信息——如何关键地影响任务是否可被指定。尽管以往工作对这种语境选择有所描述，但本文将其提升为首要考量：引入符号体系以处理一大类问题，并考察语境对任务可表述性的决定作用。结果表明，某些任务类别仅在特定语境组合下才可被表述。
原文摘要 · Abstract (English)
There is now a large body of techniques, many based on formal methods, for describing and realizing complex robotics tasks, including those involving a variety of rich goals and time-extended behavior. This paper explores the limits of what sorts of tasks are specifiable, examining how the precise grounding of specifications, that is, whether the specification is given in terms of the robot's states, its actions and observations, its knowledge, or some other information,is crucial to whether a given task can be specified. While prior work included some description of particular choices for this grounding, our contribution treats this aspect as a first-class citizen: we introduce notation to deal with a large class of problems, and examine how the grounding affects what tasks can be posed. The results demonstrate that certain classes of tasks are specifiable under different combinations of groundings.
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