While vision-language-action (VLA) models excel at robotic manipulation, they frequently fall short when interpreting physical-property language, where adjectives such as fragile or coarse-grained specify constraints that are often visually ambiguous and should modulate behavior primarily during active contact. We therefore propose Contact-Local Semantic Actuation (CLSA), an architecture that extracts semantically structured tactile representations through a language-guided bottleneck and injects them through a contact-gated residual path, confining physical adjustments to contact-relevant stages without perturbing stable nominal motion. To probe instruction-conditioned behavior under matched contexts, we introduce the Action-Property-Constraint (APC) hierarchy. On the language side, APC contrasts action-only (L0), property-aware (L1), and constraint-explicit (L2) prompts; on the trajectory side, offline evaluation uses the corresponding demonstrated behavior strata defined by the data protocol. Across matched offline comparisons, a 140-trial comparative real-robot benchmark, and a separate 240-rollout APC sweep on four tactile-sensitive tasks, CLSA improves instruction sensitivity while maintaining action accuracy. We also report visually ambiguous black-bag trials within the comparative benchmark as a diagnostic of touch-based material recognition after contact.