Dynamic Roll-and-Rise Motion by an Adult-Size Humanoid Robot

DOI Web Site 12 Citations 27 References
  • Kuniyoshi Yasuo
    School of Interdisciplinary Information Studies, The Univ. of Tokyo Dept. of Mechano-Informatics, School of Information Science and Technology, The Univ. of Tokyo
  • Ohmura Yoshiyuki
    Dept. of Mechano-Informatics, School of Information Science and Technology, The Univ. of Tokyo
  • Terada Koji
    Dept. of Mechano-Informatics, School of Information Science and Technology, The Univ. of Tokyo
  • Nagakubo Akihiko
    Intelligent Systems Division, National Institute for Advanced Industrial Science and Technology

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  • 等身大ヒューマノイドロボットによるダイナミック起き上がり行動の実現
  • トウシンダイ ヒューマノイドロボット ニ ヨル ダイナミック オキアガリ コウドウ ノ ジツゲン

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Abstract

Dexterous dynamic actions will be very important for future humanoid robots to support human tasks in unstructured environments. Such skills are difficult to achieve by the current standard control strategy for humanoid robots based on the asymptotic convergence to the successive desired states. An alternative approach would be to exploit the natural interaction dynamics between the body and the environment, and to navigate through multiple dynamics by imposing least control in order to robustly reach the goal state. As a first example of such a strategy, we propose and investigate a “Roll-and-Rise”motion. It is a fully dynamic whole-body task including underactuated motion whose state trajectory is unsolvable, and unpredictable perturbations due to complex contacts with the ground. First, human strategy is analyzed by motion capture experiments. It suggests a non-uniform control strategy which focuses on sparse critical points in the global phase space, and allows deviations and trade-offs at other parts. Then, the dynamics of the Roll-and-Rise motion is analyzed using simplified models and simulations to confirm the above findings. Finally, experiments with a real adult-size humanoid robot are successfully carried out. The robot rose from a flat-lying posture to a crouching posture within 2 seconds. Collected data is analyzed to show that uniform measures such as overall trajectory difference do not separate successes from failures, but a localized measure called the energy input timings does.

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