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Related Experiment Videos

Object grasping using the minimum variance model.

Gavin Simmons1, Yiannis Demiris

  • 1Department of Electrical and Electronic Engineering, Imperial College, London, UK.

Biological Cybernetics
|February 16, 2006
PubMed
Summary
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This study models grasping as pointing movements, successfully replicating human grip characteristics. The minimum variance model explains how grip size and timing relate to object size, offering insights into visuomotor control.

Area of Science:

  • Robotics
  • Neuroscience
  • Biomechanics

Background:

  • Grasping is traditionally viewed as two distinct visuomotor processes: transport and grip.
  • An alternative perspective suggests grasping involves digit-pointing movements.
  • Previous work established a minimum variance model for robot arm reaching.

Purpose of the Study:

  • To extend the minimum variance model for simulating grasping movements in a hand-arm system.
  • To investigate the computational model's ability to capture human grasping characteristics.
  • To analyze the contributions of transport and grip components to movement variance.

Main Methods:

  • Implemented a minimum variance optimal control scheme for a hand-arm model.
  • Planned reach and grasp components separately using a unified pointing model.

Related Experiment Videos

  • Incorporated signal-dependent noise in motor commands as per the model's requirements.

    • Main Results:

    • The model accurately reproduced key human grasping behaviors.
    • Demonstrated that maximum grip size scales with object size (slope ~0.8).
    • Showed maximum grip aperture occurs at 60-80% of movement time.

    Conclusions:

    • The minimum variance model provides a unified framework for reach-to-grasp movements.
    • The model offers insights into the interplay between transport and grip phases.
    • Further research can explore model extensions for complex grasping scenarios.