Abstract
Human upright posture is an unstable position: Continuous activation of postural muscles is required to avoid falling down. This is the output of a complex control system that monitors a very large number of inputs, related to the orientation of the body segments, to produce an adequate output as muscle activation. Complexity arises because of the very large number of correlated inputs and out puts: The finite contraction and release time of muscles and the neural control loop delays make the problem even more difficult. Nevertheless, upright posture is a capability that is learned in the first year of life. Here, the learning process is investigated by using a neural network model for the controller and the reinforcement learning paradigm. To this end, after creating a mechanically realistic digital human body, a feedback postural controller is defined, which outputs a set of joint torques as a function of orientation and rotation speed of the body segments. The controller is made up of a neural net work, whose `synaptic weights' are determined through trial-and-error (failure in maintaining upright posture) by using a reinforcement learning strategy. No desired control action is specified nor particular structure given to the controller. The results show that the anatomical arrangement of the skeleton is sufficient to shape a postural control, robust against torque perturbations and noise, and flexible enough to adapt to changes in the body model in a short time. Moreover, the learned kinematics closely resembles the data reported in the literature; it emerges from the interaction with the environment, only through trial-and-error. Overall, the results suggest that anatomical arrangement of the body segments may play a major role in shaping human motor control.