DATE: Dual-scale Adaptive Temporal Encoding for Tactile Property Perception

Vision-based tactile sensing enables robots to infer physical properties from contact interactions. Yet the cues needed for different properties unfold at different temporal scales. Hardness is reflected in deformation accumulated during pressing, whereas roughness is revealed by local texture and shear changes during sliding. Models that rely on a single global summary or a fixed short window can miss complementary evidence needed for these properties. To address this, we propose Dual-scale Adaptive Temporal Encoding DATE, which jointly models local contact changes and full-sequence dynamics. A frozen tactile backbone provides patch and frame-level features. A local branch applies spatiotemporal attention over patch tokens within short windows to preserve texture and shear transitions, while a global branch encodes the deformation history across the observed sequence. Property-specific gated fusion combines these temporal representations with statistical cues, allowing hardness and roughness to draw on different temporal evidence across pressing, sliding, and twisting. We further integrate the learned tactile representation with a frozen vision-language model for material description and property reasoning. Evaluations on two benchmarks with graded property labels demonstrate the effectiveness of DATE. On VitaSet, it improves hardness and roughness classification accuracy over AnyTouch by 7.38 and 5.14 percentage points, respectively. The downstream extension achieves an overall TVL description score of 5.66, exceeding TVL-LLaMA (ViT-Base) by 0.63 points.