Robots operating in confined or visually degraded environments require sensing modalities that provide reliable close-range feedback. Biologically inspired whisker sensors address this need: passive and lightweight, they encode contact geometry through time-varying signals. Spiking neural networks (SNNs) form a natural processing counterpart — also biologically motivated, they perform sparse, event-driven computation that mirrors neural processing and promises energy-efficient inference on neuromorphic hardware. Despite this complementary fit, training data for neuromorphic tactile sensing remains scarce, and fully spiking architectures have not been evaluated on whisker-based classification, leaving their practical viability an open question.
To address this gap, we collected two tactile datasets using a 10-whisker MEMS-based sensor array swept across three object geometries under varying contact conditions. A compact fully spiking network with 901 parameters achieved 100% test accuracy, outperforming larger spiking architectures; classification was reliable from as few as 20 timesteps of contact, and raw pressure signals alone were sufficient. Generalization experiments showed that sweep speed had minimal effect on performance, while indentation depth and sweep direction introduced larger domain shifts. Compared to a structurally equivalent non-spiking baseline, the spiking model matched classification accuracy with an estimated 9.5% reduction in overall inference energy under analog input and a potential 95% reduction under spike-encoded input.
These results demonstrate the feasibility of a fully bio-inspired pipeline — from biomimetic whisker sensing to spiking neural inference — for tactile object classification. ... Read more