Predictive simulation of human movement in OpenSim using floating-base task space control

Abstract

AbstractTask space control, also known as operational space control, is a useful paradigm for investigating neural control of human movement using predictive simulations. While some efforts have been made to implement task space control in the widely used open-source platform OpenSim, existing implementations do not support floating base kinematics, which is necessary for simulating gait and other types of human movement. Our aim in this work is to fill that gap. In this paper, we describe the theory and implementation of a floating base kinematics task space framework for torque- and muscle-driven simulations in OpenSim. Our framework builds on previous work that was limited to models with a base (i.e., root) segment fixed to ground. In addition, we integrate various algorithms from robotics in order to handle dynamically changing contacts and task prioritization. The framework can be used to generate realistic walking gaits by prescribing a small set of controller gains and gait parameters such as step length, step width and center of mass velocity. Task can be specified as desired positions, rotations, or higher-order feature such as base of support and whole-body angular momentum. We provide several examples to demonstrate how framework is successful in orchestrating a complex hierarchy of tasks that work in concert to perform both balance control and gait generation. The implementation is freely available for roboticists and biomechanists to use with OpenSim.Author summaryRecent advances in computational biomechanics have provided researchers with tools capable of predicting human movement. Previous approaches to simulating human movement required experimental data as input and the simulation would replicate the experimental motion. This conventional approach limited the scientific insights to the specific movement recorded in the laboratory. With predictive approaches, researchers can investigate how a person might respond to various factors, such as reduced muscle strength or an assistive device such as a robotic exoskeleton. Existing approaches for generating predictive simulations utilize optimization-based approaches, which can be time-consuming and difficult to troubleshoot. Task space control is an alternative approach which is widely used in robotics. Conceptually, task space control aims to generate a simulation by specifying “tasks”, such as moving a hand or foot to a desired position, and computing the joint angles required to achieve the task. Here we aim to outline the mathematics behind task space control and demonstrate how task space control can be used to generate simulations of movements such as walking.