Probabilistic Analysis of Manipulation Tasks: A Conceptual Framework
Probabilistic Analysis of Manipulation Tasks: A Conceptual Framework
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操作任务的概率分析：概念框架
DOI：
10.1177/027836499601500101
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发表时间：
1996
期刊：
影响因子：
--
通讯作者：
Alan D. Christiansen
中科院分区：
文献类型：
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作者：
R. Brost;Alan D. Christiansen
本文解决了存在不确定性的情况下的操纵计划问题。我们首先回顾 Lozano-Pérez 等人提出的最坏情况规划技术。 （1984）并表明这些方法受到最坏情况分析技术固有的信息差距的限制。随着任务不确定性的增加，即使可能存在高质量的计划，这些方法也无法产生有用的信息。为了填补这一空白，我们提出了概率反投影的概念，它描述了给定动作从给定初始状态实现任务目标的可能性。我们提供了操作任务机制的概率反投影和相关概率模型的建设性定义，并展示了这些模型如何统一和增强过去在操作规划中的一些结果。这些模型捕捉了任务行为的基本性质，但看起来非常复杂。我们展示了实验室实验的结果，包括超过 100,000 次抓取试验，测量了不同条件下抓取任务的概率反投影。所得数据支持概率反投影模型，并说明了需要概率分析的任务。我们概述了计算这些模型并使用它们构建多步骤计划的方法。
This article addresses the problem of manipulation planning in the presence of uncertainty. We begin by reviewing the worst-case planning techniques introduced by Lozano-Pérez et al. (1984) and show that these methods are limited by an information gap inherent to worst-case analysis techniques. As the task uncertainty increases, these methods fail to produce useful information, even though a high-quality plan may exist. To fill this gap, we present the notion of a probabilistic back projection, which describes the likelihood that a given action will achieve the task goal from a given initial state. We provide a constructive definition of the probabilistic backprojection and related probabilistic models of manipulation task mechanics and show how these models unify and enhance several past results in manipulation planning. These models capture the fundamental nature of the task behavior but appear to be very complex. We present the results of laboratory experiments, comprising over 100,000 grasping trials, that measured the probabilistic backprojection of a grasping task under varying conditions. The resulting data support the probabilistic back projection model and illustrate a task in which probabilistic analysis is required. We sketch methods for computing these models and using them to construct multiple-step plans.