SuperTac -通过降维实现触觉数据的超分辨率。

IF 3 Q2 ROBOTICS
Frontiers in Robotics and AI Pub Date : 2025-06-26 eCollection Date: 2025-01-01 DOI:10.3389/frobt.2025.1552922
Neel Patel, Rwik Rana, Deepesh Kumar, Nitish V Thakor
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引用次数: 0

摘要

人工触觉传感器在空间分辨率和时间分辨率之间的权衡限制了机器人和假肢触觉传感技术的发展。为了解决这一限制,我们提出了SuperTac,这是一种新型的触觉超分辨率框架,可以增强传感器固有分辨率之外的触觉感知。与现有的方法不同,SuperTac结合了降维和先进的上采样,在不影响性能的情况下提供高分辨率的触觉信息。SuperTac从人类触觉系统中机械感受器的时空处理中获得灵感,弥合了传感器限制和实际应用之间的差距。在这项研究中,一个内置了4 × 4触觉传感器阵列的主动机器人手指系统被用来触诊纹理表面。该系统包括一个触觉传感器阵列,安装在一个弹簧加载的机器人手指上,连接到一个3D打印机喷嘴,用于精确的空间控制,生成时空触觉地图。这些地图由SuperTac进行处理,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块(RIRB)。该框架产生超分辨率触觉图像(16 × 16),在保持实时使用的计算效率的同时,实现了空间分辨率的四倍提高。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进行纹理分类的准确率提高了17%。分类精度的显著提高突出了SuperTac在机器人操作、物体识别和触觉探索方面的应用潜力。通过使机器人能够感知和解释高分辨率触觉数据,SuperTac标志着弥合人类和机器人触觉能力之间差距的一步,推进了机器人在现实场景中的感知。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

SuperTac - tactile data super-resolution via dimensionality reduction.

SuperTac - tactile data super-resolution via dimensionality reduction.

SuperTac - tactile data super-resolution via dimensionality reduction.

SuperTac - tactile data super-resolution via dimensionality reduction.

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.

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来源期刊
CiteScore
6.50
自引率
5.90%
发文量
355
审稿时长
14 weeks
期刊介绍: Frontiers in Robotics and AI publishes rigorously peer-reviewed research covering all theory and applications of robotics, technology, and artificial intelligence, from biomedical to space robotics.
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