An equilibrium point based humanoids control model

Despite various models proposed for human movement control, few tackle the intricacies of the human musculoskeletal system itself. In the human musculoskeleton system, a huge amount of energy can be stored passively in the biomechanics of the muscle system. Controlling such a system in a way that takes advantage of the stored energy has led to the Equilibrium Point Hypothesis (EPH). This hypothesis holds that the central nervous system computes the equilibrium points (EPs) for a task and movements are achieved by passively attracting the muscular system to those EPs. In the forty years since the EPH was initially proposed by Feldman, the focus of research has been on testing the validity of the EPH, and much less research has been directed to how EPs are calculated as part of a motor plan. The central motor planning issue is how to deploy the huge number of degrees of freedom (DOFs) of the musculoskeletal system to achieve motor goals. This thesis proposes that humans solve this problem by motor simulation. EPs are computed prior to motor execution during two phases of motor planning. In motor simulation, gradient descent I first used to steer the end effectors on an abstract musculoskeletal model to their targets to generate an initial inverse kinematics solution. This solution might not be biologically reasonable. We add one more step of energy optimization to adjust the solution to an optimal one. The resultant configuration provides a set of EPs for an executable movement. In movement execution, damped springs are simulated as the abstraction of actual muscles. Given the EPs planned in the first phase, spring natural lengths are configured and movements are generated as far as the spring actual lengths are deviated from the EPs. Demonstrations using our model show that it can unify the control of various motions, such as reaching, walking, grasping, object manipulation, sitting and rising.;Furthermore, our model is simple and powerful enough to synthesize whole body movements in humanoids with high DOFs. We propose a 3-layered framework to autonomously compose complex movements from a repository of motor routines. Motor routine is a movement unit which implements a functional task and only involves those active joints participating in the task. We present three different ways of motor synergies over multiple motor routines to compose complex movements in a 33-DOF humanoid.