HAPTIC DETECTION OF OBJECT AFFORDANCES BY A MULTI-FINGERED ROBOT HAND

DOI Web Site 被引用文献3件
  • YASUO KUNIYOSHI
    School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bukyo-ku, Tokyo, 113-8656, Japan
  • RYO FUKANO
    School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bukyo-ku, Tokyo, 113-8656, Japan
  • TAKUYA OTANI
    School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bukyo-ku, Tokyo, 113-8656, Japan
  • TAKUMI KOBAYASHI
    School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bukyo-ku, Tokyo, 113-8656, Japan
  • NOBUYUKI OTSU
    School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bukyo-ku, Tokyo, 113-8656, Japan

抄録

<jats:p> This paper proposes a learning method for detecting object affordances through haptic exploration by a multi-fingered robot hand. Learning how to remove a screw cap from a bottle is the present target task. Assuming that coarse manipulation strategy is given by other methods, such as visual observation of a model task, the system applies coarse grabbing actions to the target object. In response to the exploratory actions, the object moves (rotates, in this case) along the physical constraint (screw). The robot detects the resulting motion through proprioception of the compliant fingers. A non-supervised statistical learning method is applied to categorize the resulting motion. The method is a combination of high-order local autocorrelation (HLAC), principal components analysis (PCA), and mean-shift clustering. Experiments with a real multi-fingered robot hand and bottle caps of different diameters confirm that the proposed method can detect and categorize rotational constraints. </jats:p>

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