An approach for goal-oriented neuromuscular control of digital humans in physics-based simulations

Abstract

We present a novel approach for the control of digital humans modelled as musculoskeletal systems in physics-based environments. This approach uses high-level goal-oriented commands as input, from which low-level neuromuscular excitations are produced that generate motion consistent with the high-level command input. Central to our approach is the reformulation of a neuromuscular control algorithm in task space, a space defined by coordinates relevant to the specific task being performed, rather than the full configuration space of the digital human. A control methodology is also detailed for addressing holonomic constraints in the skeletal kinematics. Examples are presented that demonstrate the motion control architecture for an arm-reaching simulation.