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.

人类计算机界面和人类机器人界面无处不在，从智能手机到智能汽车到外骨骼再到肌电界面。随着这些接口变得越来越复杂，工程师将界面的可调参数与人类的复杂目标，奖励和动态相匹配变得越来越困难，对于随机（噪声触发）接口而言。人类运动理论的最新进展已经在输入参数（如运动和力量）与人们关心的事物（例如准确性，精力和响应能力）之间产生了简单的因果映射。现有的人机界面设计通常仅关注一个方面（例如最大化精度），而不会利用我们对这些动态映射的理解来最大程度地提高最终用户价值的回报。 我的长期目标是翻转人机界面设计的范式，以便工程师使用这些动力学映射来形成最终用户关心的内容与工程师可以调节的参数之间的桥梁。我的短期目标是：1。因果关系，高效，人类的开发模型，并结合了信号腐败引起的人机接口不确定性。我们将使用数值有效的方法合并计算电机控制领域的分支。 2. Refine计算机控制实验。我们的初步工作表明，由于潜意识的用户适应性，可以通过完善实验技术和使用随机过程分析技术来减轻一些常规方法导致偏见。 3.开发一个平台，该平台能够对人机接口进行个性化调整。我们将开发具有数值高效的技术，以找到给定奖励的最佳映射，从而使用户可以通过阐明其相对奖励偏好来实时安全地调整参数。 4.扩展我们对具有长期目标（例如教练）的任务方法。与国际象棋算法看起来足够远以赢得游戏的方式相似，我们将使用最近的进步，这些进步在选择培训行动时隐含地考虑了潜在的学习收益。这种方法将使教练能够选择针对个人长期动态和个人目标量身定制的培训行动。我们将使用适当的统计设计和考虑EDI因素进行比较我们的解决方案与常规技术的解决方案进行验证。这个广泛的程序利用人类运动科学来告知与人类相互作用的设备的最佳设计。它代表着我们对设计的看法的高度原始范式转变，这将有助于在包括智能手机，人机界面以及辅助/增强设备在内的多个领域的突破性进步，例如外骨骼等设备，并导致具体的技术，这些技术可以解决关键的社会知识，以使其与个人复杂的设备相吻合，以使其与个人复杂的设备进行协调。