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在上一期AI文献推荐中，我们为您推荐了人工智能与机器人的热点论文。在本期推荐中，我们将为您带来人工智能领域触觉应用的前沿论文。触觉传感技术是未来实现机器人智能化的关键技术之一，为了提高智能机器人的触觉，各国学者已经开始了面向触觉技术的研究。将机器视觉与触觉相结合，机器人就可以帮助人类完成更多任务。本期选取了4篇文献，介绍触觉技术在人工智能领域的最新动态，包括使用可伸缩的触觉手套学习人类抓握的特征，基于导电微结构气隙栅极和二维半导体晶体管的高灵敏度压力传感器，基于手部弯曲所产生摩擦电信号的触觉反馈智能手套，基于触觉视觉的自主机器人心内导管导航等文献，推送给相关领域的科研人员。领域一采用可伸缩的触觉手套学习人类抓握的特征 Learning the signatures of the human grasp using a scalable tactile glove Sundaram, Subramanian, etc. NATURE, 2019, 569(7758): 698-+ Humans can feel, weigh and grasp diverse objects, and simultaneously infer their material properties while applying the right amount of force-a challenging set of tasks for a modern robot(1). Mechanoreceptor networks that provide sensory feedback and enable the dexterity of the human grasp(2) remain difficult to replicate in robots. Whereas computer-vision-based robot grasping strategies(3-5) have progressed substantially with the abundance of visual data and emerging machine-learning tools, there are as yet no equivalent sensing platforms and large-scale datasets with which to probe the use of the tactile information that humans rely on when grasping objects. Studying the mechanics of how humans grasp objects will complement vision-based robotic object handling. Importantly, the inability to record and analyse tactile signals currently limits our understanding of the role of tactile information in the human grasp itself-for example, how tactile maps are used to identify objects and infer their properties is unknown(6). Here we use a scalable tactile glove and deep convolutional neural networks to show that sensors uniformly distributed over the hand can be used to identify individual objects, estimate their weight and explore the typical tactile patterns that emerge while grasping objects. The sensor array (548 sensors) is assembled on a knitted glove, and consists of a piezoresistive film connected by a network of conductive thread electrodes that are passively probed. Using a low-cost (about US$10) scalable tactile glove sensor array, we record a large-scale tactile dataset with 135,000 frames, each covering the full hand, while interacting with 26 different objects. This set of interactions with different objects reveals the key correspondences between different regions of a human hand while it is manipulating objects. Insights from the tactile signatures of the human grasp-through the lens of an artificial analogue of the natural mechanoreceptor network-can thus aid the future design of prosthetics(7), robot grasping tools and human-robot interactions(1,8-10). 阅读原文 https://www.nature.com/articles/s41586-019-1234-z The scalable tactile glove consists of a sensor array with 548 elements 领域二基于导电微结构气隙栅极和二维半导体晶体管的敏感压力传感器 Sensitive pressure sensors based on conductive microstructured air-gap gates and two-dimensional semiconductor transistors Huang, Yun-Chiao, etc. NATURE ELECTRONICS, 2020, 3(1): 59-69 Pressure sensors with a sensitivity of ~10(3)-10(7) kPa(-1), as well as rapid response speeds, low power consumption and excellent stability, can be created by integrating a conductive microstructured air-gap gate with two-dimensional semiconductor transistors. Microscopic pressure sensors that can rapidly detect small pressure variations are of value in robotic technologies, human-machine interfaces, artificial intelligence and health monitoring devices. However, both capacitive and transistor-based pressure sensors have limitations in terms of sensitivity, response speed, stability and power consumption. Here we show that highly sensitive pressure sensors can be created by integrating a conductive microstructured air-gap gate with two-dimensional semiconductor transistors. The air-gap gate can be used to create capacitor-based sensors that have tunable sensitivity and pressure-sensing range, exhibiting an average sensitivity of 44 kPa(-1) in the 0-5 kPa regime and a peak sensitivity up to 770 kPa(-1). Furthermore, by employing the air-gap gate as a pressure-sensitive gate for two-dimensional semiconductor transistors, the pressure sensitivity of the device can be amplified to ~10(3)-10(7) kPa(-1) at an optimized pressure regime of ~1.5 kPa. Our sensors also offer fast response speeds, low power consumption, low minimum pressure detection limits and excellent stability. We illustrate their capabilities by using them to perform static pressure mapping, real-time human pulse wave measurements, sound wave detection and remote pressure monitoring. 阅读原文 https://www.nature.com/articles/s41928-019-0356-5 Capacitor- and transistor-based pressure sensors with CMAGs 领域三基于手部摩擦电信号的触觉反馈智能手套 Haptic-feedback smart glove as a creative human-machine interface (HMI) for virtual/augmented reality applications Zhu, Minglu, etc. SCIENCE ADVANCES, 2020, 6(19) Human-machine interfaces (HMIs) experience increasing requirements for intuitive and effective manipulation. Current commercialized solutions of glove-based HMI are limited by either detectable motions or the huge cost on fabrication, energy, and computing power. We propose the haptic-feedback smart glove with triboelectric-based finger bending sensors, palm sliding sensor, and piezoelectric mechanical stimulators. The detection of multidirectional bending and sliding events is demonstrated in virtual space using the self-generated triboelectric signals for various degrees of freedom on human hand. We also perform haptic mechanical stimulation via piezoelectric chips to realize the augmented HMI. The smart glove achieves object recognition using machine learning technique, with an accuracy of 96%. Through the integrated demonstration of multidimensional manipulation, haptic feedback, and AI-based object recognition, our glove reveals its potential as a promising solution for low-cost and advanced human-machine interaction, which can benefit diversified areas, including entertainment, home healthcare, sports training, and medical industry. 阅读原文 https://advances.sciencemag.org/content/6/19/eaaz8693 Schematics and characterization of triboelectric finger bending sensors Triboelectric palm sensor and piezoelectric haptic stimulator 领域四基于触觉视觉的自主机器人心内导管导航 Autonomous robotic intracardiac catheter navigation using haptic vision Fagogenis, G., etc. SCIENCE ROBOTICS, 2019, 4(29) Although all minimally invasive procedures involve navigating from a small incision in the skin to the site of the intervention, it has not been previously demonstrated how this can be performed autonomously. To show that autonomous navigation is possible, we investigated it in the hardest place to do it-inside the beating heart. We created a robotic catheter that can navigate through the blood-filled heart using wall-following algorithms inspired by positively thigmotactic animals. The catheter uses haptic vision, a hybrid sense using imaging for both touch-based surface identification and force sensing, to accomplish wall following inside the blood-filled heart. Through in vivo animal experiments, we demonstrate that the performance of an autonomously controlled robotic catheter rivaled that of an experienced clinician. Autonomous navigation is a fundamental capability on which more sophisticated levels of autonomy can be built, e.g., to perform a procedure. Similar to the role of automation in a fighter aircraft, such capabilities can free the clinician to focus on the most critical aspects of the procedure while providing precise and repeatable tool motions independent of operator experience and fatigue. 阅读原文 https://robotics.sciencemag.org/content/4/29/eaaw1977 Autonomous intracardiac navigation using haptic vision

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