Simulating Interaction Movements via Model Predictive Control

We present a Model Predictive Control (MPC) framework to simulate movement in interaction with computers, focusing on mid-air pointing as an example. Starting from understanding Optimal Feedback (OFC) perspective, we assume that users aim at minimizing internalized cost function, subject the constraints imposed by human body and interactive system. Unlike previous approaches used HCI, MPC can compute optimal controls for nonlinear systems. This allows use state-of-the-art biomechanical models handle nonlinearities occur almost any Instead of torque actuation, our model employs second-order muscles acting directly joints. compare three different functions evaluate simulation against user movements study. Our results show combination distance, control, joint acceleration matches individual users’ best, predicts accuracy is within between-user variance. To aid HCI researchers designers applying approach users, techniques, or tasks, make SimMPC framework, including CFAT, tool identify maximum voluntary torques joint-actuated models, publicly available, give step-by-step instructions.