LifelongTouch: Lifecycle-Invariant Tactile Representation

Vision-based tactile sensors (VBTSs) provide rich contact information, yet their observations are susceptible to variations across sensors and state changes during long-term use. Existing tactile representation learning methods primarily focus on cross-sensor generalization, typically treating each sensor as a static domain and thus struggling with evolving sensor states during long-term deployment. To address this limitation, we propose LifelongTouch, a lifecycle-invariant tactile representation learning framework for generalization across sensors and lifecycle states. LifelongTouch jointly models contact observations and no-contact references under the current sensor state, and combines reference-conditioned physical canonicalization, physical geometry supervision, and sensor-state physical alignment to learn consistent physical representations across sensors and lifecycle states. Furthermore, we develop a lifecycle-aligned tactile simulation engine that provides cross-sensor, cross-state tactile observations with geometric supervision for representation learning. Experiments on object classification, contact pose estimation, force distribution estimation, and surface reconstruction demonstrate that LifelongTouch generalizes robustly to unseen sensors and lifecycle states, providing an effective approach toward reliable lifelong tactile perception. The data and code will be publicly released upon acceptance.