• DocumentCode
    1865551
  • Title

    Hybrid learning control techniques for the manipulation of rigid objects

  • Author

    Aicardi, M. ; Cannata, G. ; Casalino, G.

  • Author_Institution
    Dept. of Commun. Comput. & Syst. Sci., Genova Univ., Italy
  • fYear
    1993
  • fDate
    2-6 May 1993
  • Firstpage
    672
  • Abstract
    The problem of dexterous manipulation of rigid objects is discussed. A formalism suitable for representing the space of the contact forces allowing a grasped object to perform an assigned motion is presented. The advantages that can be gained using such a formalism when robust grasping planning problems are dealt with are highlighted. The question of how manipulation dexterity in performing grasping actions could be attained in cases where possible repetitions of the planned task are somehow allowed is discussed. This is done on the basis of results concerning the so-called iterative learning hybrid control theory, extended to address robotic manipulation problems
  • Keywords
    adaptive control; force control; learning (artificial intelligence); manipulators; path planning; dexterous manipulation; hybrid learning control techniques; iterative learning hybrid control theory; rigid objects; robotic manipulation; robust grasping planning problems; Automatic control; Automation; Communication system control; Contacts; Control theory; Councils; Force control; Motion control; Robots; Robust control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Robotics and Automation, 1993. Proceedings., 1993 IEEE International Conference on
  • Conference_Location
    Atlanta, GA
  • Print_ISBN
    0-8186-3450-2
  • Type

    conf

  • DOI
    10.1109/ROBOT.1993.292056
  • Filename
    292056