Vision-Based Tactile Intelligence for Robotics: Sensing, Learning, and Embodied Manipulation

2026-08-15

AI Summary

This study addresses the limitation of sparse tactile signals in complex robotic interactions by constructing an integrated vision-tactile sensing and learning system. We propose a hardware taxonomy and a hierarchical learning framework that synergize elastomeric optical sensing, tactile simulation, and cross-domain adaptation techniques. By treating simulation and data as scalable layers, this approach effectively facilitates cross-sensor transfer and sim-to-real deployment. Furthermore, this work systematically outlines the full technical stack, significantly enhancing perceptual manipulation capabilities for contact-rich tasks. By identifying open challenges and future research directions, this research provides critical theoretical and engineering foundations for advancing tactile intelligence in robotics.

Abstract

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.

Problem

• Vision-based tactile sensors
• Tactile intelligence
• Robotic manipulation
• Contact-rich tasks

Innovation

• Vision-Based Tactile Sensors
• Integrated Sensing-and-Learning System
• Hierarchical Tactile Intelligence
• Sim-to-Real Transfer
• Hardware Taxonomy