This paper presents an off-policy Gaussian Predictive Control (GPC) framework aimed at solving optimal control problems with a smaller computational footprint, thereby facilitating real-time applicability while ensuring critical safety considerations. The proposed controller imitates classical control methodologies by modeling the optimization process through a Gaussian process and employs Gaussian Process Regression to learn from the Model Predictive Control (MPC) algorithm. Notably, the Gaussian Process setup does not incorporate a built-in model, enhancing its applicability to a broad range of control problems. We applied this framework experimentally to a differential drive mobile robot, tasking it with trajectory tracking and obstacle avoidance. Leveraging the off-policy aspect, the controller demonstrated adaptability to diverse trajectories and obstacle behaviors. Simulation experiments confirmed the effectiveness of the proposed GPC method, emphasizing its ability to learn the dynamics of optimal control strategies. Consequently, our findings highlight the significant potential of off-policy Gaussian Predictive Control in achieving real-time optimal control for handling of robotic systems in safety-critical scenarios.

翻译：本文提出一种离线策略高斯预测控制（GPC）框架，旨在以更小的计算开销求解最优控制问题，从而在确保关键安全考量的同时促进实时适用性。所提出的控制器通过高斯过程对优化过程建模，模仿经典控制方法，并利用高斯过程回归从模型预测控制（MPC）算法中学习。值得注意的是，该高斯过程设置未包含内置模型，增强了其对广泛控制问题的适用性。我们通过实验将该框架应用于差动驱动移动机器人，要求其执行轨迹跟踪与避障任务。借助离线策略特性，该控制器展现出对多样化轨迹及障碍物行为的自适应能力。仿真实验证实了所提出的GPC方法的有效性，强调其学习最优控制策略动态的能力。因此，我们的研究结果凸显了离线策略高斯预测控制在下安全关键场景中实现机器人系统实时最优控制的巨大潜力。