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

使用随机最优反馈控制和计算电机控制来设计个性化和自适应人类机器人界面

• 批准号: RGPIN-2021-02625
• 负责人: Sensinger, Jonathon
• 金额: $ 3.35万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752261
• 关键词: Using; stochastic; optimal; feedback; control

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.

人机接口和人机接口无处不在，从智能手机到智能汽车，从外骨骼到肌电接口。随着这些界面变得越来越复杂，工程师们越来越难以将界面的可调参数与人类的复杂目标、奖励和动态相匹配，特别是对于随机（噪声破坏）界面。人类运动理论的最新进展已经在输入参数（如运动和力）与人们关心的事情（如准确性，努力和响应性）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（例如最大化准确性），而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。 我的长期目标是翻转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来在最终用户关心的内容和工程师可以调整的参数之间建立桥梁。我的短期目标是：1.开发因果关系、高效、人性化的模型，并将信号损坏引起的人机界面不确定性纳入其中。我们将使用数值有效的方法合并计算电机控制领域分支的进展。2.优化计算运动控制实验。我们的初步工作表明，一些传统的方法会导致偏见，由于潜意识的用户适应，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.开发一个平台，实现人机界面的个性化调整。我们将开发数字上有效的技术，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实时安全地调整参数。 4.将我们的方法扩展到具有长期目标的任务，例如辅导。与国际象棋算法如何看得足够远以赢得比赛类似，我们将使用最新的进展，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含目标的训练行动。我们的每一个目标都将通过实验进行验证，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。这个广泛的计划利用人体运动科学来告知与人类互动的设备的最佳设计。它代表了我们如何思考设计的一个高度原创的范式转变，这将有助于在包括智能手机，人机界面和辅助/增强设备（如外骨骼）在内的多个领域取得突破性进展，并导致具体的技术，以解决关键的社会经济需求，以协调设备的复杂性与个人的复杂目标。