I-Corps: Multi-modal Robot Skins for Adaptive Human-Machine Interfaces

这个I-Corps项目的更广泛的影响/商业潜力将包括创建更高性能的人机界面，不仅在机器人领域，而且在制造业，医疗保健和智能消费产品中具有广泛的应用。通过为未来的机器添加具有成本效益和可定制的感官感知（例如触摸传感）来增加人机协作，将对机器在人周围操作时的安全性产生影响，并将提高其可用性，生产力和个性化水平。广大消费者将受益于更直观、更易于学习和使用的产品界面。许多公司和政府实验室也将受益于该项目所带来的技术商业化，因为它将有助于用机器人皮肤贴片改造现有基础设施，使其更安全，更高效。这个I-Corps项目将对“电子皮肤”技术进行商业化可行性研究，该技术基于人类与机器进行物理交互的多感官感知，通过感知触摸压力，加速度、温度和接近度。 该项目使用嵌入柔性基板的内部制造的触觉传感器，结合其他类型的商用现成传感器，开发出可以大量联网和扩展的模块化系统。许多传感器组件都是3D打印的，并且可以以具有成本效益的方式改装到现有的机器上，例如机器人，自动化生产单元，电器。此外，该系统的人机界面软件涉及新颖的学习算法，可以适应用户的偏好，减少任务完成时间和用户所需的专业知识水平。自适应软件可以根据交互结果学习实时调整人机参数，并可以考虑传感器随时间的退化，用户疲劳以及其他持续且昂贵的技术挑战，这些挑战限制了该技术的广泛采用。

The broader impact/commercial potential of this I-Corps project will consist of creating higher performance human-machine interfaces, with wide-ranging applications not only in robotics, but also in manufacturing, healthcare, and intelligent consumer products. Increasing human-machine collaboration by adding cost-effective and customizable sensory perception, for instance touch sensing, to future machines will have an impact to their safety when operating around people, and will enhance their usability, productivity, and level of personalization. Consumers at large will benefit from product interfaces that are more intuitive and easier to learn and use. Numerous companies and government labs will also benefit from the commercialization of technology resulting from this project, as it will help retrofit their existing infrastructure with robotic skin patches to make it safer and more productive.This I-Corps project will undertake commercialization feasibility research for 'Electronic Skin' technologies based on multi-sensory perception of humans interacting physically with machines, through sensing touch pressure, acceleration, temperature and proximity. Using in-house fabricated tactile sensors embedded in flexible substrates, combined with other types of commercial off-the shelf sensors, this project develops modular systems that can be networked and scaled in large numbers. Numerous sensor components are 3D printed, and can be retrofitted onto existing machinery, such as robots, automated production units, appliances in a cost effective manner. Furthermore, the human-machine interface software of the system involves novel learning algorithms that can adapt to user preferences, decrease the task completion time, and the level of expertise required by users. The adaptive software learns to adjust the human-machine parameters in real-time, according to interaction results, and can take into account sensor degradation over time, user fatigue, and other persistent and costly technical challenges that have limited the widespread adoption of this technology in the past.