Human civilization started with the invention of tools which enhanced and expanded human motor capability. With the recent development of virtual reality technology and artificial intelligence, the interaction between humans and machines has become more and more intricate. A better understanding of our motor system and the way it interacts with machines will allow us to better design intelligent devices. However, previous works in motor control modeling mostly focused on linear dynamics and had limitations in incorporating the process of learning.
A musculoskeletal model based on mechanical principles and a motor control model based on Bayesian probability are proposed in this study. The probability-theoretical formulation of the problem not only facilitates the understanding of motor learning but also transforms nonlinear dynamics into linear problems. Using these models, the interactions in which both human and machine are capable of learning and adapting are formulated and analyzed. Intelligent control policies for machine imitating the human motor control are proposed. Simulation results are also presented
