Patel Neel, Rana Rwik, Kumar Deepesh, Thakor Nitish V
Department of Mechanical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat, India.
School of Biomedical Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, Uttar Pradesh, India.
Front Robot AI. 2025 Jun 26;12:1552922. doi: 10.3389/frobt.2025.1552922. eCollection 2025.
The advancement of tactile sensing in robotics and prosthetics is constrained by the trade-off between spatial and temporal resolution in artificial tactile sensors. To address this limitation, we propose SuperTac, a novel tactile super-resolution framework that enhances tactile perception beyond the sensor's inherent resolution. Unlike existing approaches, SuperTac combines dimensionality reduction and advanced upsampling to deliver high-resolution tactile information without compromising the performance. Drawing inspiration from the spatiotemporal processing of mechanoreceptors in human tactile systems, SuperTac bridges the gap between sensor limitations and practical applications. In this study, an in-house-built active robotic finger system equipped with a 4 × 4 tactile sensor array was used to palpate textured surfaces. The system, comprising a tactile sensor array mounted on a spring-loaded robotic finger connected to a 3D printer nozzle for precise spatial control, generated spatiotemporal tactile maps. These maps were processed by SuperTac, which integrates a Variational Autoencoder for dimensionality reduction and Residual-In-Residual Blocks (RIRB) for high-quality upsampling. The framework produces super-resolved tactile images (16 × 16), achieving a fourfold improvement in spatial resolution while maintaining computational efficiency for real-time use. Experimental results demonstrate that texture classification accuracy improves by 17% when using super-resolved tactile data compared to raw sensor data. This significant enhancement in classification accuracy highlights the potential of SuperTac for applications in robotic manipulation, object recognition, and haptic exploration. By enabling robots to perceive and interpret high-resolution tactile data, SuperTac marks a step toward bridging the gap between human and robotic tactile capabilities, advancing robotic perception in real-world scenarios.
机器人技术和假肢领域中触觉传感的发展受到人工触觉传感器空间分辨率和时间分辨率之间权衡的限制。为了解决这一限制，我们提出了SuperTac，这是一种新颖的触觉超分辨率框架，可增强触觉感知，超越传感器的固有分辨率。与现有方法不同，SuperTac结合了降维和先进的上采样技术，以提供高分辨率触觉信息而不影响性能。SuperTac从人类触觉系统中机械感受器的时空处理中汲取灵感，弥合了传感器限制与实际应用之间的差距。在本研究中，使用一个内部构建的配备4×4触觉传感器阵列的主动式机器人手指系统来触摸有纹理的表面。该系统包括一个安装在弹簧加载机器人手指上的触觉传感器阵列，该手指连接到3D打印机喷嘴以进行精确的空间控制，生成时空触觉图。这些图由SuperTac处理，SuperTac集成了用于降维的变分自编码器和用于高质量上采样的残差嵌套残差块（RIRB）。该框架生成超分辨率触觉图像（16×16），在保持实时使用计算效率的同时，空间分辨率提高了四倍。实验结果表明，与原始传感器数据相比，使用超分辨率触觉数据时纹理分类准确率提高了17%。分类准确率的显著提高突出了SuperTac在机器人操作、物体识别和触觉探索应用中的潜力。通过使机器人能够感知和解释高分辨率触觉数据，SuperTac朝着弥合人类和机器人触觉能力之间的差距迈出了一步，推动了现实场景中的机器人感知发展。
