A humanlike grasping force planner for object manipulation by robot manipulators

Kazuo Kiguchi, Keigo Watanabe, Kiyotaka Izumi, Toshio Fukuda

研究成果: Contribution to journalArticle査読

3 被引用数 (Scopus)


Recently robot manipulators have been expected to perform sophisticated tasks such as object manipulation, assembly tasks, or cooperative tasks with human workers. In order to realize these tasks with robot manipulators, it is important to understand the human strategy of object grasping and manipulation. In this study, we have examined how a human being decides the grasping force necessary to manipulate an unknown object in order to apply human object-grasping strategy for robotic systems. Experiments have been performed with several kinds of objects under several kinds of conditions to investigate how much grasping force human subjects generate. Adjustment strategy of human grasping force when the object is manipulated or in contact with an environment is also examined. Neural networks (the desired grasping force planner) that generate the humanlike desired grasping force are then designed for robotic systems. The effectiveness of the proposed desired grasping force planner is evaluated via experiments.

ジャーナルCybernetics and Systems
出版ステータス出版済み - 1 1 2003

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 情報システム
  • 人工知能


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