This article presents an offline solution and online approximation to the hybrid control problem of planar non-prehensile manipulation. Hybrid dynamics and underactuation are key characteristics of this task that complicate the design of feedback controllers. We show that a model predictive control approach used in tandem with integer programming offers a powerful solution to capture the dynamic constraints associated with the friction cone as well as the hybrid nature of contact. We introduce the Model Predictive Controller with Learned Mode Scheduling (MPC-LMS), which leverages integer programming and machine learning techniques to effectively deal with the combinatorial complexity associated with determining sequences of contact modes. We validate the controller design through a numerical simulation study and with experiments on a planar manipulation setup using an industrial ABB IRB 120 robotic arm. Results show that the proposed algorithm achieves closed-loop tracking of a nominal trajectory by reasoning in real-time across multiple contact modalities.

中文翻译：

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具有混合模型预测控制的反应性平面非抓握操作

本文提出了平面非抓握操纵的混合控制问题的离线解决方案和在线逼近。混合动力和欠驱动是这项任务的关键特征，使反馈控制器的设计复杂化。我们表明，与整数规划结合使用的模型预测控制方法提供了一种强大的解决方案来捕获与摩擦锥相关的动态约束以及接触的混合性质。我们引入了具有学习模式调度（MPC-LMS）的模型预测控制器，它利用整数规划和机器学习技术来有效处理与确定接触模式序列相关的组合复杂性。我们通过数值模拟研究和使用工业 ABB IRB 120 机械臂的平面操作设置实验来验证控制器设计。结果表明，所提出的算法通过跨多个接触模式的实时推理实现了标称轨迹的闭环跟踪。