Reinforcement learning control of a biomechanical model of the upper extremity.

ABSTRACT: Among the infinite number of possible movements that can be produced, humans are commonly assumed to choose those that optimize criteria such as minimizing movement time, subject to certain movement constraints like signal-dependent and constant motor noise. While so far these assumptions have only been evaluated for simplified point-mass or planar models, we address the question of whether they can predict reaching movements in a full skeletal model of the human upper extremity. We learn a control policy using a motor babbling approach as implemented in reinforcement learning, using aimed movements of the tip of the right index finger towards randomly placed 3D targets of varying size. We use a state-of-the-art biomechanical model, which includes seven actuated degrees of freedom. To deal

SUBMITTER: Fischer F 

PROVIDER: S-EPMC8280157 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature