Evaluating Tactile Foundation Models for End-to-End Imitation Learning in Contact-Rich Manipulation

Tactile sensing plays an important role in imitation learning for contact-rich manipulation by providing direct observations of physical contact with objects. Recently, several tactile foundation models (TFMs) have been proposed to learn general-purpose tactile representations through pretraining on large-scale tactile datasets. However, how these pretrained tactile representations should be leveraged for end-to-end imitation learning remains insufficiently explored. In this work, we integrate multiple TFMs into Diffusion Policy and systematically evaluate their effectiveness for contact-rich manipulation under common experimental settings. We further investigate adaptation strategies, including encoder fine-tuning and task-specific mid-training, to understand how pretrained tactile representations can be effectively used for downstream imitation learning. Through extensive evaluation across diverse manipulation tasks in both real-world and simulation environments, including more than 5,000 real-world policy rollouts, we provide empirical insights into the use of TFMs for end-to-end imitation learning. Project page: https://mmurooka.github.io/tfm4il-project-page/