打造可感知触觉、温度与气味的仿生指尖,实现超人类触感数字化。
Digitizing Touch with an Artificial Multimodal Fingertip
- 设计球形柔性触觉传感器,集成百万级触点实时捕捉多模态信号。
- 分辨率达7微米,力觉精度达1.01毫牛,可感知10千赫振动与气味。
- 内置边缘智能芯片模拟人体反射弧,适合机器人与假肢研究者使用。
触觉是感知物体属性和环境交互的关键感官。尽管人类与机器人均能通过触觉理解世界,但现有系统缺乏具备球形柔顺结构的高维多模态触觉数字化能力。本文提出一种仿生指端传感器,集成约830万触点(taxels),可响应全向触碰,捕获多模态信号,并在设备端利用人工智能实时处理数据。实验表明,该指端可分辨最小7微米的空间特征,法向与剪切力分辨率分别达1.01毫牛和1.27毫牛,可感知最高10千赫的振动,同时具备温感与嗅觉感知能力。其内置神经网络加速器如同机器人的外周神经系统,模仿人类反射弧机制。结果证明触觉数字化具备超越人类的表现潜力,有望推动机器人、虚拟现实、假肢及电商等领域发展。为促进触觉研究,项目已开源模块化平台。
原文摘要 · Abstract (English)
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.
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