Motion Generation Using Humanoid Robot with Language Understanding

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  • HAMAZONO Yumi
    Humanities and Sciences Advanced, Ochanomizu University
  • KOBAYASHI Ichiro
    Natural Science Division, Faculty of Core Research, Ochanomizu University
  • ASOH Hideki
    Department of Statistical Inference and Mathematics, The Institute of Statistical Mathematics
  • NAKAMURA Tomoaki
    Department of Statistical Inference and Mathematics, The Institute of Statistical Mathematics
  • NAGAI Takayuki
    Department of Statistical Inference and Mathematics, The Institute of Statistical Mathematics
  • MOCHIHASHI Daichi
    Department of Statistical Inference and Mathematics, The Institute of Statistical Mathematics

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  • ヒューマノイドロボットを用いた言語理解による動作生成
  • ヒューマノイドロボット オ モチイタ ゲンゴ リカイ ニ ヨル ドウサ セイセイ

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<p>Currently, the advent of the low birthrate and aging society in Japan has become a problem so it will be increased that the opportunity to resolve the problem by using robots at home. Therefore, it is convinced that robots will have more opportunities for being active at home. These days, we can get robots more inexpensively and it makes the communication between human and robot will be significant progress. When the robot is at home, the condition in which the robot can live together with residents is that the robot mimics residents’ experiences telling by words and gestures so that learns how or what to do at home.</p><p>The objective of this study is to make a robot enable to properly behave based on instructions given by people. We therefore consider a way of associating words with robot’s actions so that a robot can behave by understanding the meaning of words.</p><p>As a concrete example, we focus on various types of cooking actions represented by words with adverbial expressions and use multilayer perceptron to learn relation between adverbial expressions and robot’s actions. The meaning of elementary cooking instructions is represented with distributed semantics by means of word2vec.</p><p>To represent the actions of a robot, we have expanded the framework of Activity-Attribute Matrix (AAM) so as it can deal with the motion of actions.</p><p>We have employed multilayer perceptron to learn the correspondence between those actions and the meaning of the instructions, and confirmed how much the actions that a robot has never done can be precisely estimated with the meaning of the given unknown instructions with the learned model.</p>

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