UniTacVLA: Unified Tactile Understanding and Prediction in Vision Language Action Models
For robotic manipulation tasks requiring dexterous contact, this work addresses the limitation of current VLA models that treat tactile signals as passive inputs, enabling more robust and accurate physical interaction.
UniTacVLA introduces a unified tactile learning framework for contact-rich dexterous manipulation that models tactile signals as dynamic interaction cues for both contact understanding and prediction. It achieves improved success rate, manipulation accuracy, and contact robustness over existing methods across four categories of contact-rich tasks under both clean and externally perturbed settings.
Vision-language-action (VLA) models have achieved strong performance in many robotic manipulation tasks, yet remain limited in contact-rich dexterous manipulation. To overcome this limitation, recent vision-tactile-language-action (VTLA) methods incorporate tactile sensing into VLA models to provide direct contact information. However, they typically treat tactile signals as passive auxiliary inputs, making it difficult to model tactile semantics and future physical interactions. To this end, we propose a unified tactile learning framework for contact-rich manipulation that models tactile signals as dynamic interaction cues for both contact understanding and prediction. Specifically, we construct a unified tactile latent space and jointly model current tactile states and future contact changes through tactile chain-of-thought reasoning and coarse-to-fine future tactile prediction, thereby forming a state-aware and dynamics-aware tactile prior. Based on this prior, we introduce a tactile-action mixed controller that combines real-time and predicted tactile feedback to refine low-frequency action chunks with high-frequency corrections. Real-world experiments on four categories of contact-rich tasks, including adjustment, insertion, wiping, and assembly, under both clean and externally perturbed settings, show that our method improves success rate, manipulation accuracy, and contact robustness over existing methods, demonstrating its effectiveness in dexterous physical interaction.