Data-driven Koopman operators for model-based shared control of human-machine systems
Data-driven Koopman operators for model-based shared control of human-machine systems
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DOI：
10.1177/0278364920921935
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发表时间：
2020-06-10
影响因子：
9.2
通讯作者：
Argall, Brenna
中科院分区：
文献类型：
--
作者：
Broad, Alexander;Abraham, Ian;Argall, Brenna
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，并实现对用户来说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，通过使用Koopman算子从观察中学习关于用户交互的动态和信息。使用学习模型，我们定义了一个优化问题来计算自治伙伴的控制策略。最后，我们动态地分配控制权的基础上，每个合作伙伴的用户输入和自主生成的控制比较。我们将这种思想称为基于模型的共享控制（MbSC）。我们评估了我们的方法与两个人类受试者的研究，共32名参与者（16名受试者在每个研究）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。第二项研究探讨了更一般的非线性变量。总体而言，我们发现，MbSC显着提高任务和控制指标相比，自然的学习，或仅用户，控制范例。我们的实验表明，通过Koopman算子学习的模型可以在用户之间推广，这表明在使用MbSC提供帮助之前没有必要从每个用户那里收集数据。我们还证明了MbSC的数据效率，因此，它在在线学习模式的有用性。最后，我们发现，非线性变量有更大的影响，用户的能力，成功地实现一个定义的任务比线性变量。
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.