From industrial exoskeletons to implantable medical devices, robots that interact closely with people are poised to improve every aspect of our lives. Yet designing these systems is very challenging; humans are incredibly complex and, in many cases, we respond to robotic devices in ways that cannot be modelled or predicted with sufficient accuracy. A new approach, human-in-the-loop optimization, can overcome these challenges by systematically and empirically identifying the device characteristics that result in the best objective performance for a specific user and application. This approach has enabled substantial improvements in human–robot performance in research settings and has the potential to speed development and enhance products. In this Perspective, we describe methods for applying human-in-the-loop optimization to new human–robot interaction problems, addressing each key decision in a variety of contexts. We also identify opportunities to develop new optimization techniques and answer underlying scientific questions. We anticipate that our readers will advance human-in-the-loop optimization and use it to design robotic devices that truly enhance the human experience.

从工业外骨骼到植入式医疗设备，与人类密切互动的机器人有望改善我们生活的各个方面。然而，设计这些系统非常具有挑战性；人类极其复杂，在许多情况下，我们对机器人设备的反应方式无法足够准确地建模或预测。人机交互优化这一新方法可以通过系统地、凭经验地识别设备特征来克服这些挑战，从而为特定用户和应用带来最佳的客观性能。这种方法极大地提高了研究环境中的人机性能，并具有加速开发和增强产品的潜力。在本视角中，我们描述了将人机循环优化应用于新的人机交互问题的方法，解决各种情况下的每个关键决策。我们还寻找开发新优化技术和回答潜在科学问题的机会。我们预计读者将推进人机交互优化，并用它来设计真正增强人类体验的机器人设备。