An Efficient Understandability Objective for Dynamic Optimal Control

Motion optimization for legible robot intent has largely ignored the robot's dynamics, citing burdensome complexity that prevents online deployment. Even where the original task (to be communicated) could be solved on the dynamical system, the legibility problem (to communicate that task's intent) could not. This work simplifies the legibility objective to have equivalent computational complexity as the original objective to be communicated. This enables any optimal control algorithm that can solve the original task to also solve the legible version of that task. Along the way, we expand the definition of "intent" to include any parameter of the optimal control problem, thereby opening the door to extend communications beyond merely desired end-points to running preferences or even, in the future, hard capabilities or safety constraints. We demonstrate how this method can replicate the properties introduced in previous communicative motion state-of-the-art (like legibility, exaggeration, and anticipation) as well as apply to non-holonomic dynamical systems.