Mobile robots face challenges when collaborating with humans in crowded and occluded environments. To tackle this issue, we propose a solution called online deep model predictive control (Deep-MPC) and apply it to human-following robots. Deep-MPC incorporates a 3-D human detector, an online learning transition model, and a data-driven MPC framework. Specifically, the 3-D human detector generates the target’s 3-D bounding box, while the transition model predicts future states, enabling anticipatory control. By combining the 3-D bounding box’s intersection over union (IoU) and state anticipation, we propose a novel evaluation metric that enhances the following robustness. The data-driven MPC framework optimizes robot actions using the neural network of the transition model, and online learning occurs through autonomous interaction with the environment, eliminating the need for system modeling and controller design. To validate our method, we conducted extensive real-world human-following experiments, demonstrating its superior performance compared to some existing methods, skeleton-based methods, and approaches without Deep-MPC.

中文翻译：

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使用在线深度模型预测控制对跟随机器人进行预期控制


移动机器人在拥挤和闭塞的环境中与人类协作时面临挑战。为了解决这个问题，我们提出了一种称为在线深度模型预测控制（Deep-MPC）的解决方案，并将其应用于人类跟随机器人。 Deep-MPC 结合了 3D 人体检测器、在线学习转换模型和数据驱动的 MPC 框架。具体来说，3D 人体检测器生成目标的 3D 边界框，而转换模型则预测未来状态，从而实现预期控制。通过结合 3-D 边界框的并集​​交集 (IoU) 和状态预期，我们提出了一种新颖的评估指标，可以增强以下鲁棒性。数据驱动的MPC框架利用过渡模型的神经网络来优化机器人动作，并通过与环境的自主交互进行在线学习，从而无需系统建模和控制器设计。为了验证我们的方法，我们进行了广泛的现实世界人类跟踪实验，证明了与一些现有方法、基于骨架的方法和没有 Deep-MPC 的方法相比，其优越的性能。