Bayesian Robots Programming
Bayesian Robots Programming
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贝叶斯机器人编程
DOI：
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
E. Mazer
中科院分区：
文献类型：
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作者：
Olivier Lebeltel;P. Bessière;Julien Diard;E. Mazer
提出了一种基于贝叶斯推理和学习的机器人编程新方法，并通过一系列复杂的实验验证了该方法的有效性。从简单的反应行为的学习，我们提出的行为组合，传感器融合，分层行为组合，情况识别和时序的实例。这一系列的实验包括一个复杂的机器人程序的增量开发的步骤。沿着这些不同的实验，讨论了这种方法的优点和缺点，并总结为结论。这些不同的机器人程序可以被看作是概率编程的一个例子，每当一个人必须处理基于不确定或不完整知识的问题时，概率编程都是适用的。可能的应用范围显然比机器人广泛得多。
We propose a new method to program robots based on Bayesian inference and learnin The capacities of this programming method are demonstrated through a succession increasingly complex experiments. Starting from the learning of simple reactive behaviors we present instances of behavior combinations, sensor fusion, hierarchical behavior com position, situation recognition and temporal sequencing. This series of experiment comprises the steps in the incremental development of a complex robot program. Th advantages and drawbacks of this approach are discussed along with these different exp iments and summed up as a conclusion. These different robotics programs may be seen an illustration of probabilistic programming applicable whenever one must deal with problems based on uncertain or incomplete knowledge. The scope of possible applications obviously much broader than robotics.