TierTac: Physically‐Grounded Hierarchical Reasoning for Generalizable Tactile Perception

Tactile perception is essential for intelligent robotic systems interacting with the physical world, yet achieving reliable understanding across heterogeneous sensors remains challenging. Existing approaches rely on representation learning or independent attribute prediction, often neglecting the physically grounded hierarchical dependencies underlying contact interactions. Here, we propose TierTac, a physically grounded hierarchical reasoning framework for generalizable tactile perception. TierTac organizes tactile understanding using a physics‐informed dependency hierarchy comprising three levels: intrinsic physical properties, compliance responses, and contact states. It combines contact‐aware, sensor‐agnostic representation learning with hierarchical neuro‐symbolic reasoning to enable physically consistent multiattribute inference. Experiments across multiple datasets demonstrate improved cross‐sensor alignment, physically consistent prediction, and generalization to unseen sensors. Real‐world robotic evaluations further show more stable and physically consistent interactions under both classical control and learned policies. These results highlight physically grounded hierarchical reasoning as an effective principle for developing generalizable tactile intelligence in robotic systems.