Using stochastic optimal feedback control and computational motor control to design personalized and adaptive human robot interfaces

从智能手机到智能汽车，从外骨骼到肌电接口，人机界面和人机界面无处不在。随着这些接口变得越来越复杂，工程师将接口的可调参数与复杂的目标、奖励和人类的动态相匹配变得越来越困难，特别是对于随机（噪声损坏）接口。人类运动理论的最新进展已经在输入参数（如运动和力量）和人们关心的东西（如准确性、努力和反应）之间产生了简单的因果关系。现有的人机界面设计通常只关注一个方面（如最大化准确性），并没有利用我们对这些动态映射的理解来最大化最终用户所看重的回报。我的长期目标是颠覆人机界面设计的范式，这样工程师就可以使用这些动态映射在最终用户关心的内容和工程师可以调整的参数之间形成一座桥梁。我的短期目标是：开发因果、高效、类人的模型，并纳入由信号损坏引起的人机界面不确定性。我们将合并跨分支的进展计算电机控制领域使用数值有效的方法。2.完善计算电机控制实验。我们的初步工作表明，由于潜意识的用户适应，一些传统方法会导致偏差，这可以通过改进实验技术和使用随机过程分析技术来减轻。3.开发一个能够个性化调整人机界面的平台。我们将开发数字高效技术，为给定的奖励找到最佳映射，允许用户通过阐明他们的相对奖励偏好来安全地实时调整参数。4.将我们的方法扩展到有长期目标的任务上，比如教练。类似于国际象棋算法如何提前看得足够远以赢得比赛，我们将使用最近的进展，在选择训练动作时隐含地考虑潜在的学习收益。这种方法将使教练能够选择适合个人长期动态和隐性目标的训练行动。我们的每个目标都将通过实验验证，将我们的解决方案与传统技术进行比较，使用适当的统计设计并考虑EDI因素。这个广泛的程序利用人类运动科学来告知与人类互动的设备的最佳设计。它代表了我们如何看待设计的一个高度原创的范式转变，将有助于在智能手机、人机界面和外骨骼等辅助/增强设备等多个领域取得突破性进展，并带来具体的技术，解决关键的社会经济需求，使设备的复杂性与个人的复杂目标相协调。

Human-computer interfaces and human-robot interfaces are everywhere, ranging from smart-phones to intelligent cars to exoskeletons to myoelectric interfaces. As these interfaces become more sophisticated, it becomes harder for engineers to match the tunable parameters of the interface to the complex goals, rewards, and dynamics of humans-particularly for stochastic (noise-corrupted) interfaces. Recent advances in human-movement theory have produced simple causal mappings between input parameters (like motion and force) and the things people care about (like accuracy, effort, and responsiveness). Existing human-machine interface designs typically only focus on one aspect (such as maximizing accuracy) and do not leverage our understanding of these dynamical mappings to maximize the rewards that end-users value. My long-term objective is to flip the paradigm of human-machine interface design, such that engineers use these dynamical mappings to form a bridge between what end-users care about and the parameters that engineers can tune. My short-term objectives are: 1.Develop models that are causal, efficient, human-like, and incorporate human-machine interface uncertainty caused by signal corruption. We will merge advances across branches of the field of computational motor control using numerically efficient methods. 2.Refine computational motor control experiments. Our preliminary work suggests that some conventional methods result in bias due to subconscious user adaptation, which can be mitigated by refining experimental techniques and using random-process analytical techniques. 3.Develop a platform that enables personalized tuning of human-machine interfaces. We will develop numerically efficient techniques that find optimal mappings for given rewards, allowing the user to safely adjust parameters in real-time by articulating their relative reward preferences. 4.Extend our approach to tasks with long-term goals, such as coaching. Similar to how chess algorithms look far enough ahead to win a game, we will use recent advances that implicitly factor in potential learning gains when choosing training actions. This approach will enable coaches to choose training actions that are tailored to the long-term dynamics and implicit goals of individuals. Each of our aims will be validated across experiments comparing our solutions to conventional techniques, using appropriate statistical design and considering EDI factors. This broad program leverages human movement science to inform the optimal design of devices that interact with humans. It represents a highly original paradigm shift in how we think about design that will contribute to groundbreaking advances in several fields including smartphones, human-robot interfaces, and assistive/augmenting devices such as exoskeletons, and lead to concrete technologies that address the critical socio-economic need to harmonize the sophistication of devices with the complex goals of individuals.