We examine how a human-robot interaction (HRI) system may be designed when input-output data from previous experiments are available. In particular, we consider how to select an optimal impedance in the assistance design for a cooperative manipulation task with a new operator. Due to the variability between individuals, the design parameters that best suit one operator of the robot may not be the best parameters for another one. However, by incorporating historical data using a linear auto-regressive (AR-1) Gaussian process, the search for a new operator's optimal parameters can be accelerated. We lay out a framework for optimizing the human-robot cooperative manipulation that only requires input-output data. We establish how the AR-1 model improves the bound on the regret and numerically simulate a human-robot cooperative manipulation task to show the regret improvement. Further, we show how our approach's input-output nature provides robustness against modeling error through an additional numerical study.

翻译：我们研究了当先前实验的输入-输出数据可用时，如何设计人机交互（HRI）系统。具体而言，我们考虑如何为与新操作员的协作操控任务选择最优阻抗以辅助设计。由于个体间存在差异，最适合某位机器人操作员的设计参数可能不适用于另一位操作员。然而，通过利用线性自回归（AR-1）高斯过程整合历史数据，可加速搜索新操作员的最优参数。我们建立了一个仅需输入-输出数据即可优化人机协作操控的框架。我们证明了AR-1模型如何改进遗憾界，并通过数值模拟人机协作操控任务展示了遗憾值的改善。此外，我们通过额外数值研究表明，我们方法的输入-输出特性对建模误差具有鲁棒性。