GhostTac：无物理接触的触觉传感器操纵技术
GhostTac: Manipulating Tactile Sensors without Physical Contact
查看机构详情
- Zhejiang University（浙江大学）
- Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
研究人员提出首个非接触式触觉传感器操纵攻击GhostTac，利用电磁干扰引发测量偏差，在15个传感器上验证有效性，揭示机器人触觉传感的新型物理攻击向量。
中文摘要 AI 辅助
触觉传感器是现代机器人系统的核心组成部分，使机器人能够通过触觉反馈感知并与物理环境交互。然而，触觉传感器的物理层安全却鲜少受到关注。我们提出了GhostTac，据我们所知，这是首个通过电磁干扰（EMI）操纵触觉传感的非接触式攻击方法。GhostTac利用非线性整流和有限带宽放大，将精心设计的EMI信号转换为持续的直流偏移，该偏移可绕过机载滤波并引发稳定的测量偏差。它通过在目标位置塑造干扰的空间分布和幅度，实现对传感器输出的细粒度、可控操纵。这种操纵可能引发有害的机器人行为，包括可能损坏物体或伤害人员的过大作用力。我们在10个传感器模块和两个灵巧手上对GhostTac进行了评估，覆盖了15个不同类型的触觉传感器，证明其在所有测试设备上均具有一致的有效性。涉及触觉抓取、滑动检测和材料分类的三个案例研究进一步说明了其对实际机器人任务的实际影响。这些发现揭示了针对机器人系统中触觉传感的一种新型物理攻击向量。
英文摘要
Tactile sensors are integral components of modern robotic systems, enabling robots to perceive and interact with the physical environment through tactile feedback. Despite their importance, the physical-layer security of tactile sensors has received little attention in prior work. In this paper, we present GhostTac, to the best of our knowledge, the first contactless attack that manipulates tactile sensing via electromagnetic interference (EMI). We identify that EMI exploits the nonlinear rectification and limited bandwidth amplification effects, allowing carefully crafted EMI signals to be converted into a persistent DC offset that bypasses on-board filtering and induces stable measurement deviations. Building on this mechanism, GhostTac enables fine-grained and controllable manipulation of sensor outputs by reshaping the spatial distribution and manipulating the magnitude at the targeted location. Such interference can induce unintended and harmful robot behaviors, such as causing a domestic robot to exert excessive force, resulting in physical damage or human injury. We evaluate GhostTac on 10 sensor modules and 2 dexterous hands, covering 15 tactile sensors of different types, and demonstrate consistent attack effectiveness across all tested devices. We further present three case studies on tactile grasping, slip detection, and material classification to illustrate practical impacts in real robotic tasks. We envision that our findings shed light on a new physical attack vector against tactile sensing in robotic systems.
