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Updated: May 25, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
Published on: April 21, 2023
Cortical network modeling for inverse kinematic computation of an anthropomorphic finger
Rodolphe J Gentili1, Hyuk Oh, Javier Molina
1Department of Kinesiology and the Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742, USA. rodolphe@umd.edu
Abstract:
The performance of reaching movements to visual targets requires complex kinematic mechanisms such as redundant, multijointed, anthropomorphic actuators and thus is a difficult problem since the relationship between sensory and motor coordinates is highly nonlinear. In this article, we present a neural model able to learn the inverse kinematics of a simulated anthropomorphic robot finger (ShadowHand™ finger) having four degrees of freedom while performing 3D reaching movements. The results revealed that this neural model was able to control accurately and robustly the finger when performing single 3D reaching movements as well as more complex patterns of motion while generating kinematics comparable to those observed in human. The long term goal of this research is to design a bio-mimetic controller providing adaptive, robust and flexible control of dexterous robotic/prosthetics hands.
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