用人工多模态指尖实现触觉数字化
Digitizing Touch with an Artificial Multimodal Fingertip
AI总结：
本研究提出一种人工多模态指尖传感器，集成高分辨率触觉感知与设备端AI，实现超人类触觉数字化，并开源平台以推动相关研究。
摘要：
触觉是一种关键的感知模态，能够提供关于物体属性以及与物理环境互动的丰富信息。人类和机器人都受益于利用触觉来感知和与周围环境互动（Johansson 和 Flanagan，2009；Li 等人，2020；Calandra 等人，2017）。然而，现有系统均未通过半球形柔性结构提供丰富、多模态的数字触觉感知能力。在此，我们描述了几项概念和技术创新，以改进触觉的数字化。这些进展体现在一个具有先进感知能力的人工手指形传感器上。重要的是，该指尖包含高分辨率传感器（约830万个触觉单元），能够响应全方位触摸，捕获多模态信号，并使用设备端人工智能实时处理数据。评估表明，该人工指尖能够分辨小至7微米的空间特征，以1.01毫牛和1.27毫牛的精度分别感知法向力和剪切力，感知高达10千赫兹的振动，感知热量，甚至感知气味。此外，它嵌入了一个设备端人工智能神经网络加速器，作为机器人上的外周神经系统，模拟人类中发现的反射弧。这些结果证明了以超人类性能实现触觉数字化的可能性。其影响深远，我们预期在机器人技术（工业、医疗、农业和消费级）、虚拟现实和远程呈现、假肢以及电子商务中具有潜在应用。为了大规模实现触觉数字化，我们开源了一个模块化平台，以促进未来关于触觉本质的研究。
英文摘要：
Touch is a crucial sensing modality that provides rich information about object properties and interactions with the physical environment. Humans and robots both benefit from using touch to perceive and interact with the surrounding environment (Johansson and Flanagan, 2009; Li et al., 2020; Calandra et al., 2017). However, no existing systems provide rich, multi-modal digital touch-sensing capabilities through a hemispherical compliant embodiment. Here, we describe several conceptual and technological innovations to improve the digitization of touch. These advances are embodied in an artificial finger-shaped sensor with advanced sensing capabilities. Significantly, this fingertip contains high-resolution sensors (~8.3 million taxels) that respond to omnidirectional touch, capture multi-modal signals, and use on-device artificial intelligence to process the data in real time. Evaluations show that the artificial fingertip can resolve spatial features as small as 7 um, sense normal and shear forces with a resolution of 1.01 mN and 1.27 mN, respectively, perceive vibrations up to 10 kHz, sense heat, and even sense odor. Furthermore, it embeds an on-device AI neural network accelerator that acts as a peripheral nervous system on a robot and mimics the reflex arc found in humans. These results demonstrate the possibility of digitizing touch with superhuman performance. The implications are profound, and we anticipate potential applications in robotics (industrial, medical, agricultural, and consumer-level), virtual reality and telepresence, prosthetics, and e-commerce. Toward digitizing touch at scale, we open-source a modular platform to facilitate future research on the nature of touch.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
