Learning the signatures of the human grasp using a scalable tactile glove
Learning the signatures of the human grasp using a scalable tactile glove
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DOI：
10.1038/s41586-019-1234-z
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发表时间：
2019-05-30
期刊：
影响因子：
64.8
通讯作者：
Matusik, Wojciech
中科院分区：
文献类型：
--
作者：
Sundaram, Subramanian;Kellnhofer, Petr;Matusik, Wojciech
人类可以感知、称重和抓握不同的物体，并在施加适量的力的同时推断其材料特性——这对于现代机器人来说是一组具有挑战性的任务(1)。提供感觉反馈并实现人类抓握灵活性的机械感受器网络(2)仍然难以在机器人中复制。尽管随着大量视觉数据和新兴机器学习工具的出现，基于计算机视觉的机器人抓取策略 (3-5) 取得了长足的进步，但目前还没有等效的传感平台和大规模数据集来探索人类在抓取物体时所依赖的触觉信息的使用。研究人类如何抓取物体的机制将补充基于视觉的机器人物体处理。重要的是，目前无法记录和分析触觉信号限制了我们对触觉信息在人类抓握本身中的作用的理解，例如，触觉地图如何用于识别物体并推断其属性尚不清楚(6)。在这里，我们使用可扩展的触觉手套和深度卷积神经网络来表明，均匀分布在手上的传感器可用于识别单个物体，估计其重量并探索抓取物体时出现的典型触觉模式。传感器阵列（548 个传感器）组装在针织手套上，由压阻膜组成，压阻膜通过被动探测的导电线电极网络连接。使用低成本（约 10 美元）可扩展触觉手套传感器阵列，我们记录了包含 135,000 帧的大规模触觉数据集，每个帧覆盖整只手，同时与 26 个不同的物体交互。这组与不同物体的交互揭示了人手在操纵物体时不同区域之间的关键对应关系。因此，通过自然机械感受器网络的人工模拟的镜头，对人类抓取的触觉特征的洞察可以帮助假肢（7）、机器人抓取工具和人机交互（1,8-10）的未来设计。
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).