Motion recognition by combining HMM and reinforcement learning
Motion recognition by combining HMM and reinforcement learning
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DOI：
10.1109/icsmc.2004.1401029
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发表时间：
2004-12
期刊：
影响因子：
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通讯作者：
Kazuhisa Hamamoto;K. Morooka;H. Nagahashi
中科院分区：
文献类型：
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作者：
Kazuhisa Hamamoto;K. Morooka;H. Nagahashi
在特定的环境中，很难预先给机器人所有可能的运动。因此，机器人需要学习如何识别其他动作，并自主生成自己的动作，才能正常工作。这些学习算法需要一种有效的方法来使识别和运动生成协同工作，因为它们需要大量的计算资源。本文主要研究一种基于世代的识别方法。我们的系统由识别和生成两个模块组成。Fanner和后者分别由从左到右的隐马尔可夫模型(HMM)和强化学习(RL)构造。当识别模块中的隐马尔可夫模型工作不充分时，在生成模块中使用RL的状态值函数来重新估计隐马尔可夫模型的模型参数。该方法能够提高隐马尔可夫模型的可靠性。
It is difficult to give a robot all possible motions beforehand in a certain environment. Therefore, the robot needs to learn how to recognize other motions and to generate its own motions autonomously for working well. These learning algorithms need an efficient way to make recognition and generation of motions work together, because they take many computing resources. This paper focuses on a generation-based recognition. Our system consists of recognition and generation modules. The fanner and latter are constructed from left-to-right hidden Markov models (HMM) and reinforcement learning (RL), respectively. When a HMM in recognition module does not work enough, the model parameters of HMM are re-estimated by using a state-value function of RL in generation module. The proposed method enables us to improve the reliability of the HMM.