We present a data-driven shared control algorithm that can be used to improve a human operator’s control of complex dynamic machines and achieve tasks that would otherwise be challenging, or impossible, for the user on their own. Our method assumes no a priori knowledge of the system dynamics. Instead, both the dynamics and information about the user’s interaction are learned from observation through the use of a Koopman operator. Using the learned model, we define an optimization problem to compute the autonomous partner’s control policy. Finally, we dynamically allocate control authority to each partner based on a comparison of the user input and the autonomously generated control. We refer to this idea as model-based shared control (MbSC). We evaluate the efficacy of our approach with two human subjects studies consisting of 32 total participants (16 subjects in each study). The first study imposes a linear constraint on the modeling and autonomous policy generation algorithms. The second study explores the more general, nonlinear variant. Overall, we find that MbSC significantly improves task and control metrics when compared with a natural learning, or user only, control paradigm. Our experiments suggest that models learned via the Koopman operator generalize across users, indicating that it is not necessary to collect data from each individual user before providing assistance with MbSC. We also demonstrate the data efficiency of MbSC and, consequently, its usefulness in online learning paradigms. Finally, we find that the nonlinear variant has a greater impact on a user’s ability to successfully achieve a defined task than the linear variant.

中文翻译：

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用于基于模型的人机系统共享控制的数据驱动 Koopman 算子

我们提出了一种数据驱动的共享控制算法，可用于改善人类操作员对复杂动态机器的控制，并完成对用户而言具有挑战性或不可能完成的任务。我们的方法假设没有系统动力学的先验知识。相反，关于用户交互的动态和信息都是通过使用 Koopman 算子从观察中学习的。使用学习到的模型，我们定义了一个优化问题来计算自主合作伙伴的控制策略。最后，我们根据用户输入和自主生成的控件的比较动态地为每个合作伙伴分配控制权限。我们将这种想法称为基于模型的共享控制 (MbSC)。我们通过由 32 名参与者（每项研究 16 名）组成的两项人类受试者研究来评估我们的方法的有效性。第一项研究对建模和自主策略生成算法施加了线性约束。第二项研究探索了更一般的非线性变体。总的来说，我们发现与自然学习或仅用户控制范式相比，MbSC 显着改善了任务和控制指标。我们的实验表明，通过 Koopman 算子学习的模型可以在用户之间进行泛化，这表明在为 MbSC 提供帮助之前没有必要从每个用户收集数据。我们还展示了 MbSC 的数据效率，以及它在在线学习范式中的有用性。最后，