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What do you really want to do? Towards a Theory of Intentions for Human-Robot Collaboration
Annals of Mathematics and Artificial Intelligence ( IF 1.2 ) Pub Date : 2020-03-24 , DOI: 10.1007/s10472-019-09672-4
Rocio Gomez , Mohan Sridharan , Heather Riley

The architecture described in this paper encodes a theory of intentions based on the key principles of non-procrastination, persistence, and automatically limiting reasoning to relevant knowledge and observations. The architecture reasons with transition diagrams of any given domain at two different resolutions, with the fine-resolution description defined as a refinement of, and hence tightly-coupled to, a coarse-resolution description. For any given goal, nonmonotonic logical reasoning with the coarse-resolution description computes an activity, i.e., a plan, comprising a sequence of abstract actions to be executed to achieve the goal. Each abstract action is implemented as a sequence of concrete actions by automatically zooming to and reasoning with the part of the fine-resolution transition diagram relevant to the current coarse-resolution transition and the goal. Each concrete action in this sequence is executed using probabilistic models of the uncertainty in sensing and actuation, and the corresponding fine-resolution outcomes are used to infer coarse-resolution observations that are added to the coarse-resolution history. The architecture’s capabilities are evaluated in the context of a simulated robot assisting humans in an office domain, on a physical robot (Baxter) manipulating tabletop objects, and on a wheeled robot (Turtlebot) moving objects to particular places or people. The experimental results indicate improvements in reliability and computational efficiency compared with an architecture that does not include the theory of intentions, and an architecture that does not include zooming for fine-resolution reasoning.

中文翻译:

你真的想做什么?走向人机协作意图理论

本文中描述的架构编码了一种基于非拖延、持久性和自动将推理限制为相关知识和观察的关键原则的意图理论。架构以两种不同分辨率的任何给定域的转换图为依据,精细分辨率描述定义为粗分辨率描述的细化,因此与粗分辨率描述紧密耦合。对于任何给定的目标,具有粗分辨率描述的非单调逻辑推理计算活动,即计划,包括要执行以实现目标的一系列抽象动作。通过自动缩放到与当前粗分辨率转换和目标相关的精细分辨率转换图部分并对其进行推理,每个抽象动作都被实现为一系列具体动作。该序列中的每个具体动作都是使用传感和驱动中不确定性的概率模型来执行的,并且使用相应的精细分辨率结果来推断添加到粗分辨率历史中的粗分辨率观测。该架构的功能在办公领域协助人类的模拟机器人、操作桌面物体的物理机器人 (Baxter) 以及将物体移动到特定地点或人的轮式机器人 (Turtlebot) 的背景下进行评估。
更新日期:2020-03-24
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