OcTac: An Octopus Sucker-Inspired Vision-Based Tactile Sensor with Self-Adaptive Adhesion for Complex Surface Interactions

Abstract

Vision-based tactile soft sensors are increasingly applied to robotic perception and manipulation by leveraging high-resolution imaging during contact with environmental surfaces, thereby enabling more adaptable and robust inter-actions.Nonetheless, ensuring optimal contact force to achieve uniform, conformal, and stable contact between sensors and surfaces remains a key challenge, particularly within complex and unstructured environments. Inspired by the highly versatile suction cups of biological octopuses for environmental surface sensing, we introduce OcTac, a prototype that seamlessly com-bines adaptive adhesion capabilities with vision-based tactile perception. OcTac harnesses its self-guided adhesion mechanism and the intrinsic ffexibility of soft materials to autonomously achieve alignment with target surfaces, even when initially misaligned at signiffcant angles—facilitating tactile perception without relying on precise external control. We conducted experiments demonstrating that OcTac exhibits robust adaptive adhesion and self-detachment capabilities on surfaces with inclination angles ranging from 0° to 90°, as well as on surfaces with varying levels of roughness (with particle sizes up to 150 µm). On challenging inclined surfaces, OcTac’s self-aligning adhesion mechanism enables stable and uniform con-tact,achieving a signiffcant improvement in image uniformity by a factor of 4.53 compared to conventional vision-based tactile soft sensors. Additionally, we demonstrated OcTac mounted on a continuum soft robotic arm, enabling it to navigate around obstacles and perform surface perception, object recognition, and grasping tasks. This work presents a new approach for achieving adaptive tactile perception in complex environments by harnessing the inherent physical intelligence of soft adhesive materials.