PR2 looking at things — Ensemble learning for unstructured information processing with Markov logic networks
PR2 looking at things — Ensemble learning for unstructured information processing with Markov logic networks
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PR2 着眼于事物——使用马尔可夫逻辑网络进行非结构化信息处理的集成学习
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
M. Beetz
中科院分区：
文献类型：
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作者：
D. Nyga;Ferenc Bálint;M. Beetz
我们研究了机器人对现实场景和日常使用物体的感知和推理任务。这项任务隐含的一个关键问题是物体的各种可感知属性，如它们的形状、纹理、颜色、大小、文本和标志，这些都超出了个人最先进的感知方法的能力。一种有希望的替代方案是采用更专业的感知方法的组合。在本文中，我们提出了一种新的组合方法，该方法分两步构建感知，并将该方法应用于我们的对象感知系统中。在第一步中，专门的方法用符号信息片段来注释检测到的对象假设。在第二步骤中，通过推断条件概率P(Q|E)来回答给定的查询Q，其中E是被认为是条件概率的证据的符号信息片段。在该设置中，Q和E是场景、对象及其注释的概率模型的一部分，感知方法预先学习了其联合概率分布。与其他方法相比，我们提出的方法在可回答的问题的通用性、能够主动引导感知的信息的产生、易于扩展、可以包含更多种类的证据以及实现自我完善和专业化的感知系统方面具有显著的优势。对于作为概率推理的一个子类的对象分类，我们表明，结合采用的专家感知方法，以协同的方式可以获得令人印象深刻的分类性能。
We investigate the perception and reasoning task of answering queries about realistic scenes with objects of daily use perceived by a robot. A key problem implied by the task is the variety of perceivable properties of objects, such as their shape, texture, color, size, text pieces and logos, that go beyond the capabilities of individual state-of-the-art perception methods. A promising alternative is to employ combinations of more specialized perception methods. In this paper we propose a novel combination method, which structures perception in a two-step process, and apply this method in our object perception system. In a first step, specialized methods annotate detected object hypotheses with symbolic information pieces. In the second step, the given query Q is answered by inferring the conditional probability P(Q | E), where E are the symbolic information pieces considered as evidence for the conditional probability. In this setting Q and E are part of a probabilistic model of scenes, objects and their annotations, which the perception method has beforehand learned a joint probability distribution of. Our proposed method has substantial advantages over alternative methods in terms of the generality of queries that can be answered, the generation of information that can actively guide perception, the ease of extension, the possibility of including additional kinds of evidences, and its potential for the realization of self-improving and - specializing perception systems. We show for object categorization, which is a subclass of the probabilistic inferences, that impressive categorization performance can be achieved combining the employed expert perception methods in a synergistic manner.