TacFRS: Plug-in Flow Reversal for High-Frequency Tactile Revision of VLA Models

Vision-language-action (VLA) models offer strong behavioral priors for robotic manipulation, but contact-rich tasks demand rapid feedback that infrequent visual policy updates cannot readily provide. We introduce Tactile Flow Reversal Steering (TacFRS), a lightweight framework that equips pretrained flow-based VLAs with high-frequency tactile action correction while keeping the base policy frozen. TacFRS uses finite-step flow reversal as an action encoding mechanism, mapping predicted action chunks into observation-conditioned structured noise. This representation connects a slow VLA to a fast tactile-conditioned decoder through an asymmetric encoding–decoding architecture. Using this representation, the latest tactile observations, and proprioception, the decoder revises each action between base-policy updates without requiring the VLA to learn a new latent action space. To preserve pretrained behavior while enabling contact-dependent adaptation, we introduce tactile-gated supervision that interpolates between base-policy actions and expert demonstrations according to tactile deviation from a contact-free reference, together with conditional flow matching and endpoint reconstruction objectives. Across four real-world single-arm and bimanual manipulation tasks, TacFRS improves every evaluated task for both backbones, increasing average success from 53.75% to 67.50% for ${\pi }_{0.5}$ and from 28.75% to 41.25% for SmolVLA. Offline analyses further show that low tactile deviations leave base-policy actions nearly unchanged, whereas high deviations induce larger corrections that reduce ground-truth action error on average.