A biped static balance control and torque pattern learning under unknown periodic external forces

  • Authors:

  • Satoshi Ito;Tomohiro Kashima;Minoru Sasaki

  • Affiliations:

  • Department of Human and Information Systems, Faculty of Engineering, Gifu University, Yanagido 1-1, Gifu 501-1193, Japan;Department of Human and Information Systems, Faculty of Engineering, Gifu University, Yanagido 1-1, Gifu 501-1193, Japan;Department of Human and Information Systems, Faculty of Engineering, Gifu University, Yanagido 1-1, Gifu 501-1193, Japan

  • Venue:
  • Engineering Applications of Artificial Intelligence
  • Year:
  • 2010

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Abstract

This paper addresses a biped balancing task in which an unknown external force is exerted, using the so-called 'ankle strategy' model. When an external force is periodic, a human adaptively maintains the balance, next learns how much force should be produced at the ankle joint from its repeatability, and finally memorized it as a motion pattern. To acquire motion patterns with balancing, we propose a control and learning method: as the control method, we adopt ground reaction force feedback to cope with an uncertain external force, while, as the learning method, we introduce a motion pattern generator that memorizes the torque pattern of the ankle joint by use of Fourier series expansion. In this learning process, the period estimation of the external force is crucial; this estimation is achieved based on local autocorrelation of joint trajectories. Computer simulations and robot experiments show effective control and learning results with respect to unknown periodic external forces.