Desired Trajectory and Sensory Feedback Control Law Synthesis for an Origami-Folding Robot based on the Statistical Feature of Direct Teaching by a Human

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  • Tanaka Kenta
    Department of Mechanical Engineering and Science, Graduate School of Engineering, Kyoto University
  • Kihara Yasuyuki
    Department of Mechanical Engineering and Science, Graduate School of Engineering, Kyoto University
  • Yokokohji Yasuyoshi
    Department of Mechanical Engineering and Science, Graduate School of Engineering, Kyoto University

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Other Title
  • 人間の直接教示動作の統計的性質に基づいた折り紙ロボットの目標軌道とセンサフィードバック則生成法
  • ニンゲン ノ チョクセツ キョウジ ドウサ ノ トウケイテキ セイシツ ニ モトズイタ オリガミ ロボット ノ モクヒョウ キドウ ト センサ フィードバックソク セイセイホウ

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Abstract

We developed a robotic hand that folds an origami form “Tadpole”. However, the robot, which simply replays a given trajectory, often fails in folding due to the fluctuation of origami paper behavior. In this paper, we propose a novel method to synthesize a desired trajectory and sensory feedback control laws for robots based on the statistical feature of direct teaching data demonstrated by a human. Hidden Markov Model (HMM) is used to model the statistical feature of human motion. Nominal desired trajectory is obtained by temporally normalizing and spatially averaging the teaching data. Sensory feedback control laws are then synthesized based on the output probability density function parameters of the HMM. Considering velocity variance and canonical correlation between velocity and force of the teaching data, important motion segments are extracted and feedback control laws are applied only for those segments. Experimental results showed that the success rate and folding quality of “Valley-fold” were improved by the proposed method. The proposed method enables robot motion teachers to simply perform direct teaching several times to transfer their skill, which is difficult to describe explicitly, to the robot.

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