发表机构
Great Bay University; Tsinghua University; The University of Hong Kong; Nanyang Technological University; The Hong Kong Polytechnic University; South China University of Technology; KTH Royal Institute of Technology; King’s College London（大湾区大学; 清华大学; 香港大学; 南洋理工大学; 香港理工大学; 华南理工大学; 瑞典皇家理工学院; 伦敦国王学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述调研机器人领域基于视觉的触觉传感器（VBTSs）全流程，对其硬件分类、学习方法、仿真与数据集等展开分析，指出开放挑战，以推进接触密集型机器人任务的触觉智能。
AI 中文摘要
触觉感知对于机器人在接触密集型任务中至关重要，但许多触觉传感器仍提供稀疏、低维的信号，无法捕捉复杂机器人感知与交互所需的充足信息。基于视觉的触觉传感器（VBTSs）是一种有力替代方案，它将柔性界面因接触产生的形变转化为图像。这种基于图像的形式赋予VBTSs高分辨率、信息丰富的触觉观测结果，可支持复杂机器人任务。本综述对完整的VBTS流程进行了调研，将感知硬件、学习方法、仿真及数据集视为一个集成的感知-学习系统：1）按可变形弹性体设计、传感器尺寸与形状、光学系统设计对代表性VBTSs进行硬件分类，以指导未来传感器开发；2）从底层信号理解到任务级策略与基础模型，呈现基于学习的触觉智能的分层视角；3）研究仿真平台与触觉数据集作为扩展层，以及用于训练、基准测试与部署的仿真到真实迁移和跨传感器适配。最后，本综述指出了VBTS在机器人领域的开放挑战与未来方向，通过提供硬件、AI架构、仿真与数据集如何交互的整体视角，旨在推进接触密集型机器人任务的触觉智能。
英文摘要
Tactile sensing is essential for robots in contact-rich tasks, yet many tactile sensors still provide sparse, low-dimensional signals that do not capture sufficient information for complex robotic perception and interaction. Vision-based tactile sensors (VBTSs) offer a powerful alternative by con-verting contact-induced deformation of a soft interface into im-ages. The image-based formulation gives VBTSs high-resolution, information-rich tactile observations that enable complex robotic tasks. This review surveys the full VBTS pipeline and treats sensing hardware, learning methods, simulation, and datasets as an integrated sensing-and-learning system. We 1) organize representative VBTSs into a hardware taxonomy structured by deformable elastomer design, sensor size and shape, and optical system design to guide future sensor development; 2) present a hierarchical view of learning-based tactile intelligence from low-level signal understanding to task-level policies and foundation models; and 3) examine simulation platforms and tactile datasets as a scaling layer, together with sim-to-real transfer and cross-sensor adaptation for training, benchmarking, and deployment. Finally, we identify open challenges and future directions for VBTSs in robotics. By providing a holistic view of how hardware, AI architectures, simulation, and datasets interact, this review aims to advance tactile intelligence for contact-rich robotic tasks.
