Marker Information Efficacy: guiding marker design through deformation information encoding in vision-based tactile sensing

Abstract Tactile perception is fundamental to manipulation and sensing, yet marker design in vision-based tactile sensors (VTS) remains largely unexplored. Conventional approaches assume that marker displacement alone suffices to characterise soft tissue deformation, without optimising marker geometry or spatial distribution. We introduce a systematic and design-centric framework based on Marker Information Efficacy (MIE) that formalises how marker properties govern the encoding of deformation information. Starting from generic marker matrices, we systematically examine how marker geometry and spatial distribution affect deformation encoding, force prediction, and computational efficiency. This analysis reveals that marker performance depends on how effectively marker design captures the dominant structure of the deformation field, thereby enabling the principled design of deformation-aligned markers for different sensing scenarios. As a representative case, the derived deformation-aligned marker demonstrates that MIE-guided marker design enables accurate force prediction using simpler predictive models with reduced computational cost. These findings establish MIE as a general and extensible design framework for VTS, effectively transforming optical markers from passive tracking tools into deformation-information-encoding components, and enabling efficient, real-time tactile sensing in contact-rich robotic manipulation.