基于模型预测控制的交互运动仿真
Simulating Interaction Movements via Model Predictive Control
摘要：
我们提出了一种方法来模拟与计算机交互的运动，使用模型预测控制（MPC）。该方法从最优反馈控制（OFC）的角度理解交互开始。我们假设用户的目标是最小化的内部成本函数，受到人体和交互系统的约束。与以前HCI中使用的线性方法相比，MPC可以计算非线性系统的最优控制。这使我们能够使用最先进的生物力学模型，并处理几乎任何交互系统中出现的非线性。而不是扭矩驱动，我们的模型采用二阶肌肉直接作用于关节。我们比较了三种不同的成本函数，并评估了模拟轨迹对用户的运动在菲茨定律类型的指向研究与四种不同的交互技术。我们的研究结果表明，距离，控制和联合加速度成本的组合匹配个人用户的运动最好，并预测运动的准确性是在用户之间的方差。为了帮助HCI研究人员和设计人员，我们介绍CFAT，一种新的方法来确定最大自愿扭矩的联合驱动模型的实验数据的基础上，并给出了实际的建议，如何模拟人类运动的不同用户，交互技术和任务。
英文摘要：
We present a method to simulate movement in interaction with computers, using Model Predictive Control (MPC). The method starts from understanding interaction from an Optimal Feedback Control (OFC) perspective. We assume that users aim to minimize an internalized cost function, subject to the constraints imposed by the human body and the interactive system. In contrast to previous linear approaches used in HCI, MPC can compute optimal controls for nonlinear systems. This allows us to use state-of-the-art biomechanical models and handle nonlinearities that occur in almost any interactive system. Instead of torque actuation, our model employs second-order muscles acting directly at the joints. We compare three different cost functions and evaluate the simulated trajectories against user movements in a Fitts' Law type pointing study with four different interaction techniques. Our results show that the combination of distance, control, and joint acceleration cost matches individual users' movements best, and predicts movements with an accuracy that is within the between-user variance. To aid HCI researchers and designers, we introduce CFAT, a novel method to identify maximum voluntary torques in joint-actuated models based on experimental data, and give practical advice on how to simulate human movement for different users, interaction techniques, and tasks.
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