TACO: TActile World Model as a Self-COrrector for Scalable Robot Policy Post-Training

Abstract

Vision-Language-Action models and World Action Models have shown promising generalization in robotic manipulation but remain fragile in contact-rich tasks, where contact perturbations can cause failures that are difficult to detect from vision alone. Corrective post-training with tactile feedback can improve recovery, but scaling such supervision through human intervention is costly. World models can synthesize additional training data, yet vision-only generation may produce visually plausible but contact-inconsistent trajectories. We therefore introduce TACO, a scalable robot policy post-training framework built on a compositional tactile world model. Given real rollouts, TACO follows a Recognize--Imagine--Label loop: an inverse dynamics and value model identifies failure-adjacent states using progress estimates, a visuo-tactile generation model imagines local corrections by jointly generating video and tactile sequences, and the inverse dynamics and value model labels them with corrective actions and progress scores. Candidates are filtered for kinematic feasibility and tactile plausibility, then selected by predicted progress gain. TACO aggregates demonstrations, real rollouts, and selected corrections for iterative post-training. It combines knowledge-insulated tactile adaptation with CFG-RL using binary advantage labels while keeping the pretrained VLM backbone fixed. Experiments on real-world tasks show that TACO improves the average task score from 0.375 to 0.825 after two post-training iterations.