A humanlike grasping force planner for object manipulation by robot manipulators

Kazuo Kiguchi, Keigo Watanabe, Kiyotaka Izumi, Toshio Fukuda

Research output: Contribution to journalArticlepeer-review

3 Citations (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.

Original languageEnglish
Pages (from-to)645-662
Number of pages18
JournalCybernetics and Systems
Issue number8
Publication statusPublished - Jan 1 2003
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Artificial Intelligence


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