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