TAD: Teacher-Assistant Distillation of Tactile Predictions for Robot Control

Tactile sensing provides complementary information for contact-rich manipulation, and predictions of tactile change offer an additional source of supervision for robot policies. However, forecasting and control impose different requirements on a representation, making it challenging to preserve predictive knowledge while adapting it to action learning. We propose Teacher-Assistant Distillation (TAD), which transfers tactile predictions into a vision–language–action policy through a frozen teacher and an adaptive assistant. The teacher predicts features of future tactile changes from current observations and initially supervises intermediate predictive tokens in the policy. During training, a teacher-initialized assistant adapts through action supervision while remaining anchored to the teacher. An exponential moving average of the assistant's prediction parameters provides distillation targets, while the online assistant's action readout supplies residual corrections to the denoising field. Asymmetric attention isolates predictive tokens from noisy-action inputs while allowing action tokens to attend to them. Instantiated on ${\pi }_{0.5}$, TAD achieves mean success rates of 90.0% across eight UniVTAC tasks and 87.5% across six real-world tasks, exceeding $N_0$-VTLA by 6.9 and 10.0 percentage points, respectively. Ablations show lower success rates without the assistant, teacher-derived supervision, or future tactile targets. These findings support teacher-anchored adaptation as a mechanism for transferring tactile predictive knowledge into robot control.