Journal: Scientific Reports

Article Title: Reinforcement learning control of a biomechanical model of the upper extremity

doi: 10.1038/s41598-021-93760-1

Figure Lengend Snippet: Synthesized reaching movement. A policy implemented as a neural network computes motor control signals of simplified muscles at the joints of a biomechanical upper extremity model from observations of the current state of the upper body. We use Deep Reinforcement Learning to learn a policy that reaches random targets in minimal time, given signal-dependent and constant motor noise.

Article Snippet: Using OpenSim, we scaled the Upper Extremity Dynamic Model to this particular person.

Techniques: Synthesized, Control, Muscles