Kong Depeng, Lu Yuyao, Zhou Shuyao, Wang Mengke, Pang Gaoyang, Wang Baocheng, Chen Lipeng, Huang Xiaoyan, Lyu Honghao, Xu Kaichen, Yang Geng
State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310030, China.
School of Electrical and Computer Engineering, The University of Sydney, Sydney 2006, Australia.
Sci Adv. 2025 Jul 4;11(27):eadv2124. doi: 10.1126/sciadv.adv2124. Epub 2025 Jul 2.
High-resolution tactile perception is essential for humanoid robots to perform contact-based interaction tasks. However, enhancing resolution is typically accompanied by increasing the density of sensing nodes, large numbers of interconnecting wires, and complex signal processing modules. This work presents super-resolution (SR) tactile sensor arrays with sparsely distributed taxels powered by a universal intelligent framework. Such smart sensor systems involve a general topological optimization strategy for taxel layout design and a deep learning model called self-attention-assisted tactile SR. Driven by the proposed model, they can dynamically distinguish high-density pressure stimuli by generating 2700 virtual taxels from only 23 physical taxels. An SR scale factor of more than 115 and an average localization error of 0.73 millimeters are achieved, approximating human fingertip accuracy and surpassing current state-of-the-art solutions. This framework enhances flexible sensors with SR capabilities in a facile and energy-efficient manner, illustrating the potential to equip robots with embodied tactile perceptions.
高分辨率触觉感知对于人形机器人执行基于接触的交互任务至关重要。然而，提高分辨率通常伴随着传感节点密度的增加、大量的互连电线和复杂的信号处理模块。这项工作提出了一种超分辨率（SR）触觉传感器阵列，其稀疏分布的像素由通用智能框架供电。这种智能传感器系统涉及一种用于像素布局设计的通用拓扑优化策略和一个名为自注意力辅助触觉超分辨率的深度学习模型。在所提出的模型驱动下，它们可以通过仅从23个物理像素生成2700个虚拟像素来动态区分高密度压力刺激。实现了超过115的超分辨率比例因子和0.73毫米的平均定位误差，接近人类指尖的精度并超越了当前的最先进解决方案。该框架以简便且节能的方式增强了具有超分辨率能力的柔性传感器，展示了为机器人配备具身触觉感知的潜力。
