GuidedVTLA: Where Proactive Tactile Guidance Meets Reactive Tactile Refinement

Multi-stage contact-rich tasks are challenging since robots must coordinate global stage-wise execution with local real-time contact reaction, with each global contact stage dictating when and how to react locally. However, existing methods typically use touch as reactive feedback for action generation or refinement in a stage-agnostic manner, limiting their ability to adapt generation and refinement strategies across distinct stages. In this work, we argue that touch is like a coin with two complementary sides: locally, it serves as reactive feedback conveying contact information, while globally, its temporal dynamics act as a proactive indicator offering contact stage awareness. To unlock the latter, we explore the temporal dynamics prior of optical tactile sensing, and discover that jointly leveraging the cumulative tactile motions and their transient differences can provide robust stage guidance for both action generation and refinement. Building on this insight, we propose GuidedVTLA, a slow-fast VTLA model that integrates proactive tactile guidance with reactive tactile refinement. Specifically, we utilize the shared proactive tactile guidance to coordinate both slow and fast action experts. The slow expert uses this guidance to dynamically route a stage-aware Mixture-of-Experts, adapting action generation strategies across contact stages. The fast expert uses temporal tactile cues to refine the action chunk during execution, with the shared guidance modulating refinement strength. Furthermore, we find that action trajectory energy across stages is mainly concentrated in a low-frequency subspace. This observation motivates us to perform tactile-driven action refinement within this subspace using a compact set of spectral residual coefficients, promoting temporally smooth refinement within each action chunk. Extensive experiments involving multi-stage contact-rich tasks on two real-world robot platforms and in simulation demonstrate the superior performance and robustness of GuidedVTLA.