Situation Recognition and Behavior Induction based on Geometric Symbol Representation of Multimodal Sensorimotor Patterns
Situation Recognition and Behavior Induction based on Geometric Symbol Representation of Multimodal Sensorimotor Patterns
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DOI：
10.1109/iros.2006.282609
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发表时间：
2006-10
期刊：
影响因子：
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通讯作者：
T. Inamura;Naoki Kojo;M. Inaba
中科院分区：
文献类型：
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作者：
T. Inamura;Naoki Kojo;M. Inaba
记忆、抽象和生成传感器和运动模式的时间序列是智能机器人的一些最重要的功能，因为这些记忆对情况识别和行为决策很有用。在传统的研究中，递归神经网络常用于这类记忆功能。然而，它们不能记忆大量的模式，并且其学习算法不可靠。本文提出了一种基于隐马尔可夫模型的行为诱导和情境估计方法，隐马尔可夫模型是目前最有用的随机模型之一。我们证明了该方法的可行性：(1)识别和联想同时执行；(2)多自由度和多感觉运动模式是可接受的
Memorization, abstraction, and generation of a time-series of sensors and motion patterns are some of the most important functions for intelligent robots, because these memories are useful for situation recognition and behavior decision making. In conventional research, recurrent neural networks are often used for such memory functions. However, they cannot memorize a lot of patterns and its learning algorithm is unreliable. In this paper, we propose a method for the induction of behavior and situational estimation based on hidden Markov models, which is currently one of the most useful stochastic models. With the proposed method, we show the feasibility of: (1) Both recognition and association are executed at the same time, and (2) A multiple degrees of freedom and multiple sensorimotor patterns are acceptable