Touch2Predict: Learning Physical Properties from a Single Tactile Interaction

Estimating physical properties of deformable objects is essential for predictive robotic manipulation, but remains difficult in contact-rich interactions where visual deformation is small, localized, and often occluded. Tactile sensing provides direct measurements of mechanical response, yet these observations are highly sensitive to uncertain robot–object contact configuration, coupling pose and material estimation. We introduce Touch2Predict, a structured tactile inverse-modeling framework that separates these two factors according to their physical roles. Contact-establishment cues guide a low-dimensional derivative-free search over contact-relevant pose variables, while the full spatiotemporal pressure sequence drives adjoint-based material refinement through differentiable contact simulation. Alternating the two stages progressively recovers the interaction-specific contact state and object-level physical properties. Across eight synthetic scenes with translational, rotational, and coupled pose uncertainty, \method recovers Young's modulus with an average per-scene median relative error of 6.05%. Real-robot experiments further demonstrate its practical feasibility, with the modulus identified for a test object within 12.4% of an independent reference measurement. Together with qualitative predictions under unseen interactions, these results highlight the potential of tactile inverse physics to recover transferable physical models for understanding and predicting physical interactions.