Incremental learning of gestures for human–robot interaction
Incremental learning of gestures for human–robot interaction
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DOI：
10.1007/s00146-009-0248-8
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发表时间：
2010-05
期刊：
影响因子：
3
通讯作者：
S. Okada;Yoichi Kobayashi;S. Ishibashi;T. Nishida
中科院分区：
文献类型：
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作者：
S. Okada;Yoichi Kobayashi;S. Ishibashi;T. Nishida
要让机器人与人类同居，它应该能够从经验中学习人类的非语言社会行为。在本文中，我们提出了一种新的机器学习方法来识别用于交互和通信的手势。我们的方法使机器人能够在人机交互过程中以无监督的方式逐步学习手势。它允许用户在学习之前不定义手势的数量和类型。所提出的方法（HB-SOINN）是基于自组织增量神经网络和隐马尔可夫模型。我们在HB-SOINN中增加了一个交互式学习机制，以防止单个集群由于被分配了多个含义而导致失败。例如，一个句子：“Keep on going left slowly”有三个意思，如“Keep on（1）"，“going left（2）"，“slowly（3）"。我们通过实验测试了所提出的方法对使用运动捕捉设备测量手势获得的数据的聚类性能。结果表明，HB-SOINN的分类性能优于传统的聚类方法。此外，我们还发现交互式学习功能提高了HB-SOINN的学习性能。
For a robot to cohabit with people, it should be able to learn people’s nonverbal social behavior from experience. In this paper, we propose a novel machine learning method for recognizing gestures used in interaction and communication. Our method enables robots to learn gestures incrementally during human–robot interaction in an unsupervised manner. It allows the user to leave the number and types of gestures undefined prior to the learning. The proposed method (HB-SOINN) is based on a self-organizing incremental neural network and the hidden Markov model. We have added an interactive learning mechanism to HB-SOINN to prevent a single cluster from running into a failure as a result of polysemy of being assigned more than one meaning. For example, a sentence: “Keep on going left slowly” has three meanings such as, “Keep on(1)”, “going left(2)”, “slowly(3)”. We experimentally tested the clustering performance of the proposed method against data obtained from measuring gestures using a motion capture device. The results show that the classification performance of HB-SOINN exceeds that of conventional clustering approaches. In addition, we have found that the interactive learning function improves the learning performance of HB-SOINN.