  Tactile representation learning (TRL) equips robots with the ability to
leverage touch information, boosting performance in tasks such as environment
perception and object manipulation. However, the heterogeneity of tactile
sensors results in many sensor- and task-specific learning approaches. This
limits the efficacy of existing tactile datasets, and the subsequent
generalisability of any learning outcome. In this work, we investigate the
applicability of vision foundational models to sensor-agnostic TRL, via a
simple yet effective transformation technique to feed the heterogeneous sensor
readouts into the model. Our approach recasts TRL as a computer vision (CV)
problem, which permits the application of various CV techniques for tackling
TRL-specific challenges. We evaluate our approach on multiple benchmark tasks,
using datasets collected from four different tactile sensors. Empirically, we
demonstrate significant improvements in task performance, model robustness, as
well as cross-sensor and cross-task knowledge transferability with limited data
requirements.
